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Pipeline run

d3227c34-2a9d-49b8-b689-6dac0f8be475

Pipeline LLM cost (USD)
API 1: $0.0075 API 2: $0.0001 API 3: $0.0000 Total: $0.0076

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
SPARSE JD role baseline loaded sources · ai_index: role_baseline · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data pipeline development
Design ClickHouse schemas and run Kafka/Python/dbt/Spark pipelines for search, clickstream, and conversion data; then build Grafana/Prometheus/OpenTelemetry monitoring, data quality checks, and GDPR/CCPA controls.
"Build ETL/ELT pipelines (Kafka ClickHouse/Postgres) using Python/dbt/Spark."
Tech stack maturity
Modern Cloud Native
The stack centers on contemporary data and observability tools like Spark, dbt, Kafka, OpenTelemetry, Prometheus, and Grafana, which are characteristic of modern cloud-native engineering.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
1.20 / 5
· Title match
Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3): AI
Evidence — skills matched in JD (15)
ClickHouse Kafka Python dbt Spark Grafana OpenTelemetry Prometheus SQL PostgreSQL Microsoft SQL Server Airflow Dagster Feature Store Terraform
Skill cluster (9 dimension groups, role-scoped)
ETL and ELT Tooling
dbt Spark
Observability and Operations
Grafana Prometheus
Programming Languages for Data Work
Python SQL
Backend Observability, Logging, and Diagnostics
OpenTelemetry
Data Pipeline Orchestration
Dagster
Infrastructure as Code
Terraform
Messaging and Event Streaming
Kafka
Relational Database Usage
PostgreSQL
Cross-cutting / unaligned
ClickHouse Microsoft SQL Server Airflow Feature Store
Show KRA description ↓
Design scalable ClickHouse schemas for search, clickstream, and conversion data. Build ETL/ELT pipelines (Kafka ClickHouse/Postgres) using Python/dbt/Spark. Develop Grafana dashboards for search funnel, latency, and error monitoring. Implement OpenTelemetry across services; define Prometheus alerts. Ensure data quality, governance, and compliance (GDPR/CCPA). Mentor two junior data engineers. 4-7 years data engineering experience, ideally with ClickHouse. Kafka pipelines (Python or Kafka Connect). Strong SQL (Postgres/MSSQL). Grafana, Prometheus, and observability experience. Airflow/Dagster, dbt. Feature store exposure. Terraform basics.

Signals

Skill backend-engineer
0.55
Alias data-engineer
1.00
KRA data-engineer
0.60

Post-classification

Centroidupdated · n=123
Alias collision log
New-role queue
New skills captured2
New KRA captured

Captured for admin review

ClickHouse primary Data Engineer pending
Microsoft SQL Server primary Data Engineer pending
Status: completed Created: 2026-05-27T14:05:20.447710Z Updated: 2026-05-27T14:07:37.514155Z API 3 duration: 84718 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Data Engineer

domain · Data Engineering & Analytics CASE DOMAIN

slug: data-engineer · id: 2 · source: db

Domain=Data Engineering & Analytics; The JD centers on building scalable data pipelines, streaming ingestion, analytics engineering, observability, and data platform work, which best matches Data Engineer.

Matched skills

ClickHouseKafkaPythondbtSparkGrafanaOpenTelemetryPrometheusPostgresSQLAirflowDagsterfeature storeTerraformMSSQL

Matched dimensions

Data Pipeline EngineeringStreaming Data IngestionAnalytics Data ModelingObservability and MonitoringData Quality and GovernanceCloud/Data Platform EnablementMentoring and Team Leadership

Matched KRAs

Design scalable ClickHouse schemasBuild ETL/ELT pipelinesDevelop Grafana dashboardsImplement OpenTelemetry across servicesDefine Prometheus alertsEnsure data quality, governance, and complianceMentor two junior data engineers

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
1
Skipped

Job description

Job Title:-Senior Data & Analytics Engineer

Experience: 5-8 years

Type: Full-time

About The Role

We need a Senior Data & Analytics Engineer to build real-time behavioral data pipelines, dashboards, and observability systems that measure the impact of AI search.

Roles & Responsibilities:-

Design scalable ClickHouse schemas for search, clickstream, and conversion data.

Build ETL/ELT pipelines (Kafka ClickHouse/Postgres) using Python/dbt/Spark.

Develop Grafana dashboards for search funnel, latency, and error monitoring.

Implement OpenTelemetry across services; define Prometheus alerts.

Ensure data quality, governance, and compliance (GDPR/CCPA).

Mentor two junior data engineers.

Must-Have Skills

4-7 years data engineering experience, ideally with ClickHouse.

Kafka pipelines (Python or Kafka Connect).

Strong SQL (Postgres/MSSQL).

Grafana, Prometheus, and observability experience.

Nice-to-Have

Airflow/Dagster, dbt.

Feature store exposure.

Terraform basics.

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

ClickHouse Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Kafka Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Kafka id=36 · kafka

Aliases — catalog

  • Kafka (CANONICAL) primary

Context tags (catalog)

Apache Flink Apache Kafka Apache Pulsar Apache Spark Avro KSQL Kafka API Kafka Connect Kafka Streams ZooKeeper Zookeeper backpressure brokers consumer consumer group consumer groups event sourcing event-driven architecture exactly-once semantics fault tolerance high throughput log compaction message broker message queue microservices offsets partition partitioning partitions producer producer API real-time analytics real-time data replication schema registry stream processing topic topic partitioning topics

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Event Stream Store
Vendor
Confluent
License
apache_2
Year introduced
2011
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Kafka appears in many production JDs for event streaming and data pipelines, and remains a standard platform in cloud/vendor offerings (e.g., Confluent, AWS MSK), indicating broad hiring demand.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
3533
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Asynchronous Messaging and Event Streaming Catalog dimension db id 297

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Go Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Scala Backend Developer

  • Messaging and Background Jobs Catalog dimension db id 291

    Library dimension (catalog)

    Roles linked in library: PHP Backend Developer, Python Backend Developer, Ruby Backend Developer

  • Messaging and Event Streaming Catalog dimension db id 8

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Messaging and Background Jobs
messaging-and-background-jobs
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension saved
Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

Aliases — catalog

  • Python (CANONICAL) primary
  • Python 2 (VERSION)
  • Python 2.x (VERSION)
  • Python 3 (VERSION)
  • Python 3.10 (VERSION)
  • Python 3.11 (VERSION)
  • Python 3.12 (VERSION)
  • Python 3.x (VERSION)
  • py (VERSION)
  • py2 (VERSION)
  • py3 (VERSION)
  • python 3 (VERSION)
  • python 3.x (VERSION)
  • python2 (VERSION)
  • python3 (VERSION)
  • python3.x (VERSION)

Context tags (catalog)

API Django FastAPI Flask Jupyter NumPy PEP 8 Pandas REST SQLAlchemy asyncio pandas pip pytest type hints venv virtualenv

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
PSF
License
mit
Year introduced
1991
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3

Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Scripting & DSL Languages Catalog dimension db id 248

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • Python Programming Catalog dimension db id 290

    Library dimension (catalog)

    Roles linked in library: Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
dbt Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: dbt id=115 · dbt

Aliases — catalog

  • dbt (CANONICAL) primary

Context tags (catalog)

BigQuery Databricks ELT Jinja Redshift SQL Snowflake YAML data modeling incremental models macros snapshots sources tests warehouse

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Analytics Engineering Framework
Vendor
dbt Labs
License
apache_2
Year introduced
2016
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: dbt appears in many analytics engineer and data platform job descriptions, and its GitHub repo has strong adoption signals with widespread ecosystem support from major cloud/data vendors.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
89
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Spark Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Spark id=1350 · apache-spark

Aliases — catalog

  • Apache Spark (CANONICAL)
  • apache spark 3 (VERSION)
  • spark (VERSION)
  • spark 3 (VERSION)
  • spark 3.x (VERSION)
  • spark3 (VERSION)

Context tags (catalog)

Apache Kafka Cluster Manager DAGScheduler Data Lake DataFrame ETL Hadoop MLlib Machine Learning PySpark RDD Scala Spark SQL Spark Streaming SparkSession

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Distributed Data Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2010
Confidence
0.94
Version strategy
SEPARATE_ENTITY
Version tag
3.x

Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
1021
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Grafana Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Grafana id=47 · grafana

Aliases — catalog

  • Grafana (CANONICAL) primary

Context tags (catalog)

Alertmanager Grafana API Grafana Cloud Grafana Enterprise Grafana Loki Grafana Tempo Grafana plugins InfluxDB Kubernetes Loki Prometheus SLA SLO SRE Tempo alerting alerts annotations dashboard dashboarding dashboards data source data sources data visualization influxDB metrics monitoring observability plugins query editor templating time series time-series visualization

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Observability Platform
Vendor
Grafana Labs
License
apache_2
Year introduced
2014
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Grafana appears in many DevOps/SRE job descriptions and is a standard observability dashboarding tool alongside Prometheus and Loki; strong GitHub/community adoption and broad vendor integrations signal mainstream use.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
176
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Backend Observability, Logging, and Diagnostics Catalog dimension db id 388

    Library dimension (catalog)

    Roles linked in library: Kotlin Backend Developer, Scala Backend Developer

  • Observability and Incident Response Catalog dimension db id 10

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Node.js Backend Developer, PHP Backend Developer

  • Observability and Incident Triage Catalog dimension db id 155

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • Observability and Operations Catalog dimension db id 143

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Incident Triage
observability-and-incident-triage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
OpenTelemetry Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: OpenTelemetry id=48 · opentelemetry

Aliases — catalog

  • OpenTelemetry (CANONICAL) primary

Context tags (catalog)

API Baggage Grafana Jaeger OTLP OpenCensus OpenTelemetry Collector OpenTracing Prometheus SDK Zipkin backend integration cloud-native collector context propagation distributed systems distributed tracing exporter exporters gRPC instrumentation logging logs metrics observability prometheus propagation sampling span trace context traces tracing

Stored enrichment (catalog DB)

Category
Library
Sub-category
Observability Library
Vendor
CNCF
License
apache_2
Year introduced
2019
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: OpenTelemetry appears in many recent SRE/observability JDs and is backed by CNCF, but it’s still less universal than Prometheus/Datadog in hiring pipelines.

Skill profile (library / DB)

Skill nature
LIBRARY
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
7
Sub-category id
1687
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Backend Observability, Logging, and Diagnostics Catalog dimension db id 388

    Library dimension (catalog)

    Roles linked in library: Kotlin Backend Developer, Scala Backend Developer

  • Observability and Diagnostics Catalog dimension db id 287

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer, Java Backend Developer, Python Backend Developer

  • Observability and Incident Response Catalog dimension db id 10

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Node.js Backend Developer, PHP Backend Developer

  • Observability and Incident Triage Catalog dimension db id 155

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • Observability and Operations Catalog dimension db id 143

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Diagnostics
observability-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Incident Triage
observability-and-incident-triage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Prometheus Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Prometheus id=46 · prometheus

Aliases — catalog

  • Prometheus (CANONICAL) primary

Context tags (catalog)

Alertmanager Grafana Kubernetes OpenMetrics PromQL Pushgateway TSDB Thanos alert rules alerts cAdvisor cloud-native container orchestration dashboards exporter kube-state-metrics labels metrics metrics endpoint metrics scraping monitoring node_exporter observability open-source pushgateway recording rules remote write scrape interval scraping service discovery time series visualization

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Monitoring Platform
Vendor
Cloud Native Computing Foundation
License
apache_2
Year introduced
2012
Confidence
0.62
Version strategy
NOT_APPLICABLE

Maturity reasoning: Prometheus is widely listed in DevOps/SRE job descriptions and is a standard CNCF monitoring stack component, often paired with Grafana for production observability.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
2837
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Backend Observability, Logging, and Diagnostics Catalog dimension db id 388

    Library dimension (catalog)

    Roles linked in library: Kotlin Backend Developer, Scala Backend Developer

  • Observability and Incident Response Catalog dimension db id 10

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Node.js Backend Developer, PHP Backend Developer

  • Observability and Incident Triage Catalog dimension db id 155

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • Observability and Operations Catalog dimension db id 143

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Incident Triage
observability-and-incident-triage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL id=101 · sql

Aliases — catalog

  • SQL (CANONICAL) primary

Context tags (catalog)

ACID CTE DDL DML ETL JOIN MySQL NoSQL OLAP ORM PostgreSQL SQL injection SQLite T-SQL data modeling data warehousing database normalization execution plan indexing joins normalization query optimization stored procedures subquery transaction isolation transaction management window functions

Stored enrichment (catalog DB)

Category
Language
Sub-category
Query Language
Vendor
ANSI
License
unknown
Year introduced
1974
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: SQL appears in a large share of data, backend, and analytics job descriptions and remains the default query language for PostgreSQL, MySQL, and cloud warehouses like Snowflake/BigQuery.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
97
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
PostgreSQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: PostgreSQL id=16 · postgresql

Aliases — catalog

  • PostgreSQL (CANONICAL) primary
  • PG 13 (VERSION)
  • PG 14 (VERSION)
  • PG 15 (VERSION)
  • PG 16 (VERSION)
  • PostgreSQL 13 (VERSION)
  • PostgreSQL 14 (VERSION)
  • PostgreSQL 15 (VERSION)
  • PostgreSQL 16 (VERSION)
  • Postgres 13 (VERSION)
  • Postgres 14 (VERSION)
  • Postgres 15 (VERSION)
  • Postgres 16 (VERSION)
  • pg10 (VERSION)
  • pg11 (VERSION)
  • pg12 (VERSION)
  • pg13 (VERSION)
  • pg14 (VERSION)
  • pg15 (VERSION)
  • pg16 (VERSION)
  • postgres (VERSION)
  • postgresql 10 (VERSION)
  • postgresql 11 (VERSION)
  • postgresql 12 (VERSION)
  • postgresql 13 (VERSION)
  • postgresql 14 (VERSION)
  • postgresql 15 (VERSION)
  • postgresql 16 (VERSION)
  • postgresql-16 (VERSION)
  • postgresql10 (VERSION)
  • postgresql11 (VERSION)
  • postgresql12 (VERSION)
  • postgresql13 (VERSION)
  • postgresql14 (VERSION)
  • postgresql15 (VERSION)
  • postgresql16 (VERSION)

Context tags (catalog)

ACID EXPLAIN JSONB PL/pgSQL PostGIS SQL VACUUM backup data integrity database migration extensions indexes indexing joins migration partitioning performance tuning pgAdmin query optimization replication schema stored procedures table partitioning transaction transactions triggers views

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database
Vendor
PostgreSQL Global Development Group
License
other_open
Year introduced
1996
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: PostgreSQL appears in a large share of backend/data engineering job postings and is a default managed option across AWS RDS, GCP Cloud SQL, and Azure Database, indicating broad hiring-pipeline adoption.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
29
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

    Roles linked in library: Fullstack Developer, Fullstack Developer, PHP Backend Developer

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer

  • Relational Database Usage Catalog dimension db id 371

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Microsoft SQL Server Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL Server id=18 · sql-server

Aliases — catalog

  • SQL Server (CANONICAL) primary
  • SQL Server 2000 (VERSION)
  • SQL Server 2005 (VERSION)
  • SQL Server 2008 (VERSION)
  • SQL Server 2012 (VERSION)
  • SQL Server 2014 (VERSION)
  • SQL Server 2016 (VERSION)
  • SQL Server 2017 (VERSION)
  • SQL Server 2019 (VERSION)
  • SQL Server 2022 (VERSION)
  • SQL Server 6.5 (VERSION)
  • SQL Server 7.0 (VERSION)

Context tags (catalog)

Always On CLR Integration Clustered Index ETL Execution Plan Linked Servers Query Store Replication SQL Agent SQL Server Agent SQL Server Integration Services SQL Server Management Studio SQL Server Reporting Services SSIS SSMS SSRS Stored Procedures T-SQL TempDB backup and recovery backup and restore clustering data migration data warehousing database design database normalization indexing performance tuning query optimization replication stored procedures transaction log transaction logs

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database
Vendor
Microsoft
License
proprietary
Year introduced
1989
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: SQL Server appears in many enterprise job descriptions and remains a major Microsoft-supported RDBMS with active Azure SQL/SQL Server demand; it is a common hiring-pipeline staple, not a sunset technology.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
29
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Database Design
relational-database-design
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Airflow Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Airflow id=265 · airflow

Aliases — catalog

  • Airflow (CANONICAL) primary
  • airflow 2 (VERSION)
  • airflow-2 (VERSION)
  • airflow2 (VERSION)
  • airflow2.x (VERSION)
  • apache airflow 2 (VERSION)

Context tags (catalog)

Apache Celery CeleryExecutor DAG ETL Executor Jinja templating Python SLA Sensors UI XCom backfill connections data pipeline executor hooks logging monitoring operators plugins scheduler task dependencies task instance variables

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Workflow Orchestration Tool
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2014
Confidence
0.95
Version strategy
SEPARATE_ENTITY
Version tag
2.x

Maturity reasoning: Apache Airflow appears in many data engineering job postings and is a common orchestration choice in production stacks; its GitHub activity and ecosystem remain strong, with no vendor sunset or clear replacement dominating JDs.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
130
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Workflow Orchestration for ML Pipelines Catalog dimension db id 54

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Dagster Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Dagster id=111 · dagster

Aliases — catalog

  • Dagster (CANONICAL) primary

Context tags (catalog)

Airflow Dagit ELT ETL IO manager Prefect asset materialization asset-based orchestration backfill backfills config data lineage data pipeline data pipelines dataflow dbt event_log execution graph job jobs op ops orchestration partition partitioning pipeline repository resource resources schedule schedules sensor solid

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Data Orchestration Tool
Vendor
Elementl
License
apache_2
Year introduced
2019
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Dagster appears in a growing number of data engineering JDs and cloud vendor docs, but it is still far less common than Airflow/Prefect, indicating rising adoption rather than ubiquity.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
1161
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Pipeline Orchestration Catalog dimension db id 23

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Workflow Orchestration for ML Pipelines Catalog dimension db id 54

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Pipeline Orchestration
data-pipeline-orchestration
Existing dimension (library) · Role↔dimension saved
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Feature Store Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Feature Store id=1593 · feature-store

Aliases — catalog

  • Feature Store (CANONICAL)

Context tags (catalog)

ML model serving batch processing data governance data lineage data pipeline data quality data retrieval data versioning feature engineering feature retrieval integration metadata management model training real-time analytics schema evolution

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Feature Store
Confidence
0.78
Version strategy
NOT_APPLICABLE

Maturity reasoning: Feature stores appear in growing MLOps job postings and vendor ecosystems (e.g., Feast, Tecton, SageMaker Feature Store), but they’re still far less universal than core datastores like PostgreSQL or Redis.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
1199
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Terraform Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Terraform id=286 · terraform

Aliases — catalog

  • Terraform (CANONICAL) primary

Context tags (catalog)

AWS Azure GCP HCL IaC Terraform Cloud Terraform Enterprise Terraform Registry Terragrunt apply backend destroy infrastructure automation modules outputs plan providers provisioning remote backends remote state resource blocks resource management state file state management terraform apply terraform plan variables version control workspaces

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Infrastructure As Code Tool
Vendor
HashiCorp
License
mpl
Year introduced
2014
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: Terraform is broadly listed in DevOps/SRE/cloud JDs and remains a standard IaC tool across AWS/Azure/GCP; HashiCorp’s ecosystem and widespread GitHub usage signal strong market adoption.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
191
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Infrastructure & Security Automation Frameworks Catalog dimension db id 249

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Infrastructure as Code Catalog dimension db id 132

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, DevOps Engineer

  • Infrastructure as Code for ML Catalog dimension db id 57

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Infrastructure & Security Automation Frameworks
infrastructure-security-automation-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Infrastructure as Code
infrastructure-as-code
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Infrastructure as Code for ML
infrastructure-as-code-for-ml
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

All API 3 persistence rows

Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
Kafka in_db
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kafka in_db
Messaging and Background Jobs
messaging-and-background-jobs
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kafka in_db
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension saved
Python in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Python in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
dbt in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Grafana in_db
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Grafana in_db
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Grafana in_db
Observability and Incident Triage
observability-and-incident-triage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Grafana in_db
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
OpenTelemetry in_db
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
OpenTelemetry in_db
Observability and Diagnostics
observability-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
OpenTelemetry in_db
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
OpenTelemetry in_db
Observability and Incident Triage
observability-and-incident-triage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
OpenTelemetry in_db
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Prometheus in_db
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Prometheus in_db
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Prometheus in_db
Observability and Incident Triage
observability-and-incident-triage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Prometheus in_db
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
PostgreSQL in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Microsoft SQL Server new
Relational Database Design
relational-database-design
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Airflow in_db
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Dagster in_db
Data Pipeline Orchestration
data-pipeline-orchestration
Existing dimension (library) · Role↔dimension saved
Dagster in_db
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Feature Store in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Terraform in_db
Infrastructure & Security Automation Frameworks
infrastructure-security-automation-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Terraform in_db
Infrastructure as Code
infrastructure-as-code
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Terraform in_db
Infrastructure as Code for ML
infrastructure-as-code-for-ml
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed ClickHouse | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
dimension_skill_link_proposed Microsoft SQL Server ↔ Relational Database Design
nano JD Parser — gpt-4.1-nano click to toggle
RoleSenior Data & Analytics Engineer
Experience5-8 years
DomainOther
JD type pass
Show raw JSON
{
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  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [],
      "domain": "Other"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": 8,
    "min": 5,
    "raw": "5-8 years"
  },
  "job_locations": [],
  "role": "Senior Data \u0026 Analytics Engineer",
  "role_aliases": [
    "Data Engineer",
    "Analytics Engineer",
    "Senior Data Engineer"
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  "role_archetype": "Data",
  "roles_and_responsibilities": [
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      "bullet_count": 6,
      "heading": "Roles \u0026 Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Design scalable ClickHouse schemas for",
        "last_5_words": "two junior data engineers."
      },
      "text": "Design scalable ClickHouse schemas for search, clickstream, and conversion data.\nBuild ETL/ELT pipelines (Kafka ClickHouse/Postgres) using Python/dbt/Spark.\nDevelop Grafana dashboards for search funnel, latency, and error monitoring.\nImplement OpenTelemetry across services; define Prometheus alerts.\nEnsure data quality, governance, and compliance (GDPR/CCPA).\nMentor two junior data engineers.",
      "word_count": 56
    },
    {
      "bullet_count": 4,
      "heading": "Must-Have Skills",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "4-7 years data engineering experience,",
        "last_5_words": "and observability experience."
      },
      "text": "4-7 years data engineering experience, ideally with ClickHouse.\nKafka pipelines (Python or Kafka Connect).\nStrong SQL (Postgres/MSSQL).\nGrafana, Prometheus, and observability experience.",
      "word_count": 36
    },
    {
      "bullet_count": 3,
      "heading": "Nice-to-Have",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Airflow/Dagster, dbt.",
        "last_5_words": "basics."
      },
      "text": "Airflow/Dagster, dbt.\nFeature store exposure.\nTerraform basics.",
      "word_count": 10
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "ClickHouse"
    },
    {
      "is_primary": true,
      "skill_name": "Kafka"
    },
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "dbt"
    },
    {
      "is_primary": true,
      "skill_name": "Spark"
    },
    {
      "is_primary": true,
      "skill_name": "Grafana"
    },
    {
      "is_primary": true,
      "skill_name": "OpenTelemetry"
    },
    {
      "is_primary": true,
      "skill_name": "Prometheus"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "PostgreSQL"
    },
    {
      "is_primary": true,
      "skill_name": "Microsoft SQL Server"
    },
    {
      "is_primary": false,
      "skill_name": "Airflow"
    },
    {
      "is_primary": false,
      "skill_name": "Dagster"
    },
    {
      "is_primary": false,
      "skill_name": "Feature Store"
    },
    {
      "is_primary": false,
      "skill_name": "Terraform"
    }
  ],
  "jd_role": {
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    "rationale": null,
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    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
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        "source_marker": {
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          "last_5_words": "two junior data engineers."
        },
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        "word_count": 56
      },
      {
        "bullet_count": 4,
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        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "4-7 years data engineering experience,",
          "last_5_words": "and observability experience."
        },
        "text": "4-7 years data engineering experience, ideally with ClickHouse.\nKafka pipelines (Python or Kafka Connect).\nStrong SQL (Postgres/MSSQL).\nGrafana, Prometheus, and observability experience.",
        "word_count": 36
      },
      {
        "bullet_count": 3,
        "heading": "Nice-to-Have",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Airflow/Dagster, dbt.",
          "last_5_words": "basics."
        },
        "text": "Airflow/Dagster, dbt.\nFeature store exposure.\nTerraform basics.",
        "word_count": 10
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "d3227c34-2a9d-49b8-b689-6dac0f8be475",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 1.0,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "Analytics Engineer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 142,
        "score": 1.0,
        "slug": "analytics-engineer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
            "sentence": "Build ETL/ELT pipelines (Kafka ClickHouse/Postgres) using Python/dbt/Spark.",
            "similarity": 0.6156
          },
          {
            "kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
            "sentence": "Kafka pipelines (Python or Kafka Connect).",
            "similarity": 0.6121
          },
          {
            "kra_text": "Implements data quality validation rules, reconciliation checks, and anomaly detection to ensure data completeness, accuracy, and consistency.",
            "sentence": "Ensure data quality, governance, and compliance (GDPR/CCPA).",
            "similarity": 0.5605
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.5961,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "Backend Developer",
        "kra_matches": [
          {
            "kra_text": "Adds structured logging, metrics, distributed tracing, and alerting to improve system observability and support production debugging.",
            "sentence": "Grafana, Prometheus, and observability experience.",
            "similarity": 0.5505
          },
          {
            "kra_text": "Adds structured logging, metrics, distributed tracing, and alerting to improve system observability and support production debugging.",
            "sentence": "Implement OpenTelemetry across services; define Prometheus alerts.",
            "similarity": 0.4705
          },
          {
            "kra_text": "Integrates with third-party services, payment gateways, messaging queues like Kafka or RabbitMQ, and internal microservices via HTTP and event-driven patterns.",
            "sentence": "Kafka pipelines (Python or Kafka Connect).",
            "similarity": 0.4405
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 1,
        "score": 0.4872,
        "slug": "backend-engineer",
        "total_count": null
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "Design scalable ClickHouse schemas for search, clickstream, and conversion data.",
            "similarity": 0.4659
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "Build ETL/ELT pipelines (Kafka ClickHouse/Postgres) using Python/dbt/Spark.",
            "similarity": 0.4424
          },
          {
            "kra_text": "Debugs full-stack issues that span frontend rendering, API behavior, database queries, and infrastructure configuration to identify root causes.",
            "sentence": "Grafana, Prometheus, and observability experience.",
            "similarity": 0.4404
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 15,
        "score": 0.4496,
        "slug": "full-stack-engineer",
        "total_count": null
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Sets up model monitoring dashboards, data drift detection, prediction performance tracking, and alert routing for production ML systems.",
            "sentence": "Develop Grafana dashboards for search funnel, latency, and error monitoring.",
            "similarity": 0.4742
          },
          {
            "kra_text": "Validates model performance benchmarks, data schema contracts, and system integration health before signing off on production release readiness.",
            "sentence": "Ensure data quality, governance, and compliance (GDPR/CCPA).",
            "similarity": 0.4466
          },
          {
            "kra_text": "Orchestrates model serving deployments to production using Kubernetes, MLflow Model Registry, SageMaker, or Kubeflow Serving infrastructure.",
            "sentence": "Kafka pipelines (Python or Kafka Connect).",
            "similarity": 0.4236
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.4481,
        "slug": "ml-ops-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": [
          {
            "kra_text": "Monitors production model behavior for data drift, concept drift, and prediction performance degradation using monitoring dashboards and alerting.",
            "sentence": "Develop Grafana dashboards for search funnel, latency, and error monitoring.",
            "similarity": 0.4723
          },
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
            "sentence": "Build ETL/ELT pipelines (Kafka ClickHouse/Postgres) using Python/dbt/Spark.",
            "similarity": 0.4264
          },
          {
            "kra_text": "Monitors production model behavior for data drift, concept drift, and prediction performance degradation using monitoring dashboards and alerting.",
            "sentence": "Grafana, Prometheus, and observability experience.",
            "similarity": 0.4249
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 3,
        "score": 0.4412,
        "slug": "ml-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Backend Developer",
        "kra_matches": null,
        "matched_count": 6,
        "matched_skills": [
          "Grafana",
          "Kafka",
          "OpenTelemetry",
          "PostgreSQL",
          "Prometheus",
          "Python"
        ],
        "role_id": 1,
        "score": 0.5455,
        "slug": "backend-engineer",
        "total_count": 11
      },
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 5,
        "matched_skills": [
          "Apache Spark",
          "Kafka",
          "Python",
          "SQL",
          "dbt"
        ],
        "role_id": 2,
        "score": 0.4545,
        "slug": "data-engineer",
        "total_count": 11
      },
      {
        "display_name": ".NET Backend Developer",
        "kra_matches": null,
        "matched_count": 5,
        "matched_skills": [
          "Grafana",
          "Kafka",
          "OpenTelemetry",
          "PostgreSQL",
          "Prometheus"
        ],
        "role_id": 83,
        "score": 0.4545,
        "slug": "dotnet-backend-developer",
        "total_count": 11
      },
      {
        "display_name": "Node.js Backend Developer",
        "kra_matches": null,
        "matched_count": 5,
        "matched_skills": [
          "Grafana",
          "Kafka",
          "OpenTelemetry",
          "PostgreSQL",
          "Prometheus"
        ],
        "role_id": 82,
        "score": 0.4545,
        "slug": "node-backend-developer",
        "total_count": 11
      },
      {
        "display_name": "Kotlin Backend Developer",
        "kra_matches": null,
        "matched_count": 5,
        "matched_skills": [
          "Grafana",
          "Kafka",
          "OpenTelemetry",
          "PostgreSQL",
          "Prometheus"
        ],
        "role_id": 84,
        "score": 0.4545,
        "slug": "kotlin-server-backend-developer",
        "total_count": 11
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "DOMAIN",
    "chosen_role": {
      "display_name": "Data Engineer",
      "kra_matches": null,
      "matched_count": null,
      "matched_skills": null,
      "role_id": 2,
      "score": 0.97,
      "slug": "data-engineer",
      "total_count": null
    },
    "confidence": 0.97,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [
      "Data Pipeline Engineering",
      "Streaming Data Ingestion",
      "Analytics Data Modeling",
      "Observability and Monitoring",
      "Data Quality and Governance",
      "Cloud/Data Platform Enablement",
      "Mentoring and Team Leadership"
    ],
    "matched_kras": [
      "Design scalable ClickHouse schemas",
      "Build ETL/ELT pipelines",
      "Develop Grafana dashboards",
      "Implement OpenTelemetry across services",
      "Define Prometheus alerts",
      "Ensure data quality, governance, and compliance",
      "Mentor two junior data engineers"
    ],
    "matched_skills": [
      "ClickHouse",
      "Kafka",
      "Python",
      "dbt",
      "Spark",
      "Grafana",
      "OpenTelemetry",
      "Prometheus",
      "Postgres",
      "SQL",
      "Airflow",
      "Dagster",
      "feature store",
      "Terraform",
      "MSSQL"
    ],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Domain=Data Engineering \u0026 Analytics; The JD centers on building scalable data pipelines, streaming ingestion, analytics engineering, observability, and data platform work, which best matches Data Engineer.",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 123,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 6821,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ClickHouse",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6822,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Microsoft SQL Server",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
{
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 173,
      "existing_alias_text": "Kafka",
      "input_term": "Kafka",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "Kafka",
        "id": 36,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "kafka",
        "sub_category_id": 3533,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 67,
      "existing_alias_text": "Python",
      "input_term": "Python",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Python",
        "id": 5,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "python",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 309,
      "existing_alias_text": "dbt",
      "input_term": "dbt",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "dbt",
        "id": 115,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "dbt",
        "sub_category_id": 89,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2510,
      "existing_alias_text": "spark",
      "input_term": "Spark",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Apache Spark",
        "id": 1350,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-spark",
        "sub_category_id": 1021,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 184,
      "existing_alias_text": "Grafana",
      "input_term": "Grafana",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Grafana",
        "id": 47,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "grafana",
        "sub_category_id": 176,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 185,
      "existing_alias_text": "OpenTelemetry",
      "input_term": "OpenTelemetry",
      "matched_canonical": {
        "category_id": 7,
        "display_name": "OpenTelemetry",
        "id": 48,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LIBRARY",
        "slug": "opentelemetry",
        "sub_category_id": 1687,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 183,
      "existing_alias_text": "Prometheus",
      "input_term": "Prometheus",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Prometheus",
        "id": 46,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "prometheus",
        "sub_category_id": 2837,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 271,
      "existing_alias_text": "SQL",
      "input_term": "SQL",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "SQL",
        "id": 101,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "sql",
        "sub_category_id": 97,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 121,
      "existing_alias_text": "PostgreSQL",
      "input_term": "PostgreSQL",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "PostgreSQL",
        "id": 16,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "postgresql",
        "sub_category_id": 29,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 135,
      "existing_alias_text": "SQL Server",
      "input_term": "Microsoft SQL Server",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "SQL Server",
        "id": 18,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "sql-server",
        "sub_category_id": 29,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 526,
      "existing_alias_text": "Airflow",
      "input_term": "Airflow",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Airflow",
        "id": 265,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "airflow",
        "sub_category_id": 130,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 305,
      "existing_alias_text": "Dagster",
      "input_term": "Dagster",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Dagster",
        "id": 111,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "dagster",
        "sub_category_id": 1161,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2539,
      "existing_alias_text": "Feature Store",
      "input_term": "Feature Store",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "Feature Store",
        "id": 1593,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "feature-store",
        "sub_category_id": 1199,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 547,
      "existing_alias_text": "Terraform",
      "input_term": "Terraform",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Terraform",
        "id": 286,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "terraform",
        "sub_category_id": 191,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    }
  ],
  "candidate_roles": [
    {
      "display_name": ".NET Backend Developer",
      "id": 83,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "dotnet-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Go Backend Developer",
      "id": 81,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "go-backend-developer",
      "source": "db"
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      "display_name": "Kotlin Backend Developer",
      "id": 84,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "kotlin-server-backend-developer",
      "source": "db"
    },
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      "display_name": "Node.js Backend Developer",
      "id": 82,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "node-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Scala Backend Developer",
      "id": 87,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "scala-backend-developer",
      "source": "db"
    },
    {
      "display_name": "PHP Backend Developer",
      "id": 86,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "php-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Python Backend Developer",
      "id": 80,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "python-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Ruby Backend Developer",
      "id": 85,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "ruby-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Backend Developer",
      "id": 1,
      "rationale": null,
      "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
      "slug": "backend-engineer",
      "source": "db"
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    {
      "display_name": "Data Engineer",
      "id": 2,
      "rationale": null,
      "role_archetype": null,
      "slug": "data-engineer",
      "source": "db"
    },
    {
      "display_name": "Cloud Security Engineer",
      "id": 23,
      "rationale": null,
      "role_archetype": null,
      "slug": "cloud-security-engineer",
      "source": "db"
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    {
      "display_name": "Fullstack Developer",
      "id": 15,
      "rationale": null,
      "role_archetype": null,
      "slug": "full-stack-engineer",
      "source": "db"
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    {
      "display_name": "Fullstack Developer",
      "id": 435,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "fullstack-developer",
      "source": "db"
    },
    {
      "display_name": "Cyber Security Engineer",
      "id": 5,
      "rationale": null,
      "role_archetype": null,
      "slug": "cybersecurity-engineer",
      "source": "db"
    },
    {
      "display_name": "ML Engineer",
      "id": 3,
      "rationale": null,
      "role_archetype": null,
      "slug": "ml-engineer",
      "source": "db"
    },
    {
      "display_name": "MLOps Engineer",
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      "rationale": null,
      "role_archetype": null,
      "slug": "ml-ops-engineer",
      "source": "db"
    },
    {
      "display_name": "AR/VR Engineer",
      "id": 8,
      "rationale": null,
      "role_archetype": null,
      "slug": "ar-vr-engineer",
      "source": "db"
    },
    {
      "display_name": "DevOps Engineer",
      "id": 10,
      "rationale": null,
      "role_archetype": null,
      "slug": "devops-engineer",
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    {
      "display_name": "Cloud Architect",
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      "rationale": null,
      "role_archetype": null,
      "slug": "cloud-architect",
      "source": "db"
    },
    {
      "display_name": "Java Backend Developer",
      "id": 79,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "java-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Pega Developer",
      "id": 24,
      "rationale": null,
      "role_archetype": null,
      "slug": "pega-developer",
      "source": "db"
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  ],
  "chosen_role": {
    "display_name": "Data Engineer",
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    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on building scalable data pipelines, streaming ingestion, analytics engineering, observability, and data platform work, which best matches Data Engineer.",
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    "slug": "data-engineer",
    "source": "db"
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  "dimensions": [
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Asynchronous Messaging and Event Streaming",
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        "rationale": "Asynchronous communication patterns and broker technologies used to decouple backend services and move work off the request path. Includes queues, pub/sub, event streams, consumer groups, dead-letter queues, and delivery semantics across systems such as Kafka, RabbitMQ, NATS, SQS/SNS, Pulsar, and ActiveMQ.",
        "slug": "asynchronous-messaging-and-event-streaming",
        "source": "db"
      },
      "input_skill": "Kafka",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": ".NET Backend Developer",
          "id": 83,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "dotnet-backend-developer",
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        },
        {
          "display_name": "Go Backend Developer",
          "id": 81,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "go-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Kotlin Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "kotlin-server-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Node.js Backend Developer",
          "id": 82,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "node-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Scala Backend Developer",
          "id": 87,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "scala-backend-developer",
          "source": "db"
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      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Messaging and Background Jobs",
        "id": 291,
        "rationale": "Asynchronous processing patterns and worker systems used to decouple backend work from request handling. This is a coherent cluster because the role supports background jobs, retries, and deferred processing.",
        "slug": "messaging-and-background-jobs",
        "source": "db"
      },
      "input_skill": "Kafka",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "PHP Backend Developer",
          "id": 86,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "php-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Python Backend Developer",
          "id": 80,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "python-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Ruby Backend Developer",
          "id": 85,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "ruby-backend-developer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Messaging and Event Streaming",
        "id": 8,
        "rationale": "Transport-layer systems used to move events and decouple producers from consumers. Data engineers use these systems to ingest, buffer, and distribute event data before downstream processing.",
        "slug": "messaging-and-event-streaming",
        "source": "db"
      },
      "input_skill": "Kafka",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Developer",
          "id": 1,
          "rationale": null,
          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
          "slug": "backend-engineer",
          "source": "db"
        },
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Security Scripting \u0026 DSL Languages",
        "id": 248,
        "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
        "slug": "cloud-security-scripting-dsl-languages",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Security Engineer",
          "id": 23,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-security-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages",
        "id": 1,
        "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
        "slug": "programming-languages",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Developer",
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          "rationale": null,
          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
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        },
        {
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          "rationale": null,
          "role_archetype": null,
          "slug": "full-stack-engineer",
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        {
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          "rationale": null,
          "role_archetype": "Engineering",
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      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages and Scripting",
        "id": 59,
        "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
        "slug": "programming-languages-and-scripting",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
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          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
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      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for Data Work",
        "id": 21,
        "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
        "slug": "programming-languages-for-data-work",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
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          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
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      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for ML Systems",
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        "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
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        "source": "db"
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      "input_skill": "Python",
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      "roles_from_db": [
        {
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          "rationale": null,
          "role_archetype": null,
          "slug": "ml-engineer",
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        {
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          "rationale": null,
          "role_archetype": null,
          "slug": "ml-ops-engineer",
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    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for XR",
        "id": 97,
        "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
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        "source": "db"
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      "roles_from_db": [
        {
          "display_name": "AR/VR Engineer",
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          "rationale": null,
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          "slug": "ar-vr-engineer",
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    },
    {
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        "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
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        "source": "db"
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      "roles_from_db": [
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          "role_archetype": "Engineering",
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    },
    {
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        "difficulty_hint": "well_known",
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        "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
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        "source": "db"
      },
      "input_skill": "dbt",
      "llm_role": null,
      "roles_from_db": [
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          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
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        }
      ]
    },
    {
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        "difficulty_hint": "well_known",
        "display_name": "ETL and ELT Tooling",
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        "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
        "slug": "etl-and-elt-tooling",
        "source": "db"
      },
      "input_skill": "Spark",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
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        }
      ]
    },
    {
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        "display_name": "Backend Observability, Logging, and Diagnostics",
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        "rationale": "Instrumentation and troubleshooting practices used to understand and improve backend service behavior in production and lower environments. This includes logs, metrics, traces, alerting, dashboards, structured logging, distributed tracing, health checks, and root-cause analysis using ecosystem-specific tools such as SLF4J, Logback, Micrometer, OpenTelemetry, Prometheus, Grafana, ILogger, Serilog, and Application Insights.",
        "slug": "backend-observability-logging-and-diagnostics",
        "source": "db"
      },
      "input_skill": "Grafana",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Kotlin Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "kotlin-server-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Scala Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "scala-backend-developer",
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        }
      ]
    },
    {
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        "difficulty_hint": "well_known",
        "display_name": "Observability and Incident Response",
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        "rationale": "Instrumentation and production troubleshooting practices used to keep backend services reliable. Includes logs, metrics, traces, alerting, dashboards, and incident diagnosis.",
        "slug": "observability-and-incident-response",
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      "input_skill": "Grafana",
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      "roles_from_db": [
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          "display_name": ".NET Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
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        {
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          "rationale": null,
          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
          "slug": "backend-engineer",
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        {
          "display_name": "Node.js Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
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        },
        {
          "display_name": "PHP Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
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        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Observability and Incident Triage",
        "id": 155,
        "rationale": "Telemetry, alerting, and troubleshooting practices used to diagnose failed builds, broken deployments, and unhealthy release environments. This is a coherent cluster because delivery reliability depends on quickly identifying where the workflow failed.",
        "slug": "observability-and-incident-triage",
        "source": "db"
      },
      "input_skill": "Grafana",
      "llm_role": null,
      "roles_from_db": [
        {
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          "rationale": null,
          "role_archetype": null,
          "slug": "devops-engineer",
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    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Observability and Operations",
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        "rationale": "Monitoring, logging, tracing, and operational readiness patterns used to keep cloud platforms supportable. Cloud Architects use this to define what telemetry and operational controls workloads must expose.",
        "slug": "observability-and-operations",
        "source": "db"
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      "input_skill": "Grafana",
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      "roles_from_db": [
        {
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          "role_archetype": null,
          "slug": "cloud-architect",
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      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Backend Observability, Logging, and Diagnostics",
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      "input_skill": "OpenTelemetry",
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      "roles_from_db": [
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    {
      "dimension": {
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        "display_name": "Observability and Diagnostics",
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        "rationale": "Instrumentation and troubleshooting practices used to understand Java service behavior in production and lower environments. This cluster covers logs, metrics, traces, correlation IDs, and root-cause analysis from service telemetry.",
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      "input_skill": "OpenTelemetry",
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      "roles_from_db": [
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          "role_archetype": "Engineering",
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        {
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "java-backend-developer",
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        {
          "display_name": "Python Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
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    },
    {
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        "display_name": "Observability and Incident Response",
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        "rationale": "Instrumentation and production troubleshooting practices used to keep backend services reliable. Includes logs, metrics, traces, alerting, dashboards, and incident diagnosis.",
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      "roles_from_db": [
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        {
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          "role_archetype": "Engineering",
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    {
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    },
    {
      "dimension": {
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        "display_name": "Backend Observability, Logging, and Diagnostics",
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        "rationale": "Instrumentation and troubleshooting practices used to understand and improve backend service behavior in production and lower environments. This includes logs, metrics, traces, alerting, dashboards, structured logging, distributed tracing, health checks, and root-cause analysis using ecosystem-specific tools such as SLF4J, Logback, Micrometer, OpenTelemetry, Prometheus, Grafana, ILogger, Serilog, and Application Insights.",
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      "input_skill": "Prometheus",
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      "roles_from_db": [
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          "display_name": "Kotlin Backend Developer",
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    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Observability and Incident Response",
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        "rationale": "Instrumentation and production troubleshooting practices used to keep backend services reliable. Includes logs, metrics, traces, alerting, dashboards, and incident diagnosis.",
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        {
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    {
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        "display_name": "Observability and Incident Triage",
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        "rationale": "Telemetry, alerting, and troubleshooting practices used to diagnose failed builds, broken deployments, and unhealthy release environments. This is a coherent cluster because delivery reliability depends on quickly identifying where the workflow failed.",
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    },
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      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Pega Programming Languages \u0026 DSLs",
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        "source": "db"
      },
      "input_skill": "SQL",
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      "roles_from_db": [
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          "display_name": "Pega Developer",
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          "rationale": null,
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    },
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      "dimension": {
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      "roles_from_db": [
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        "source": "db"
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          "display_name": "Fullstack Developer",
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          "rationale": null,
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        {
          "display_name": "Fullstack Developer",
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          "rationale": null,
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          "slug": "full-stack-engineer",
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          "display_name": "PHP Backend Developer",
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    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Relational Database Design",
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        "rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
        "slug": "relational-database-design",
        "source": "db"
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      "input_skill": "PostgreSQL",
      "llm_role": null,
      "roles_from_db": [
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          "display_name": ".NET Backend Developer",
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          "role_archetype": "Engineering",
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        {
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          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
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          "display_name": "Kotlin Backend Developer",
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          "role_archetype": "Engineering",
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        {
          "display_name": "Node.js Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "node-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Python Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "python-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Ruby Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "ruby-backend-developer",
          "source": "db"
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        {
          "display_name": "Scala Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "scala-backend-developer",
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    },
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        "slug": "terraform",
        "sub_category_id": 191,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Infrastructure \u0026 Security Automation Frameworks",
            "id": 249,
            "rationale": "Frameworks and libraries for provisioning, configuring, and automating cloud security infrastructure.",
            "slug": "infrastructure-security-automation-frameworks",
            "source": "db"
          },
          "input_skill": "Terraform",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Security Engineer",
              "id": 23,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-security-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Infrastructure as Code",
            "id": 132,
            "rationale": "Declarative provisioning and environment definition tools used to codify cloud infrastructure, repeatable environments, and platform standards. Cloud Architects use these to express reference architectures and guardrails.",
            "slug": "infrastructure-as-code",
            "source": "db"
          },
          "input_skill": "Terraform",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            },
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Infrastructure as Code for ML",
            "id": 57,
            "rationale": "Tools for provisioning and managing ML infrastructure resources through code.",
            "slug": "infrastructure-as-code-for-ml",
            "source": "db"
          },
          "input_skill": "Terraform",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Terraform",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "ClickHouse"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on building scalable data pipelines, streaming ingestion, analytics engineering, observability, and data platform work, which best matches Data Engineer.",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "ClickHouse",
      "tag": "new"
    },
    {
      "skill": "Kafka",
      "tag": "in_db"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "dbt",
      "tag": "in_db"
    },
    {
      "skill": "Spark",
      "tag": "in_db"
    },
    {
      "skill": "Grafana",
      "tag": "in_db"
    },
    {
      "skill": "OpenTelemetry",
      "tag": "in_db"
    },
    {
      "skill": "Prometheus",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "PostgreSQL",
      "tag": "in_db"
    },
    {
      "skill": "Microsoft SQL Server",
      "tag": "in_db"
    },
    {
      "skill": "Airflow",
      "tag": "in_db"
    },
    {
      "skill": "Dagster",
      "tag": "in_db"
    },
    {
      "skill": "Feature Store",
      "tag": "in_db"
    },
    {
      "skill": "Terraform",
      "tag": "in_db"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Asynchronous Messaging and Event Streaming",
          "id": 297,
          "rationale": "Asynchronous communication patterns and broker technologies used to decouple backend services and move work off the request path. Includes queues, pub/sub, event streams, consumer groups, dead-letter queues, and delivery semantics across systems such as Kafka, RabbitMQ, NATS, SQS/SNS, Pulsar, and ActiveMQ.",
          "slug": "asynchronous-messaging-and-event-streaming",
          "source": "db"
        },
        "dimension_id": 297,
        "input_skill": "Kafka",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Go Backend Developer",
            "id": 81,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "go-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 36,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Messaging and Background Jobs",
          "id": 291,
          "rationale": "Asynchronous processing patterns and worker systems used to decouple backend work from request handling. This is a coherent cluster because the role supports background jobs, retries, and deferred processing.",
          "slug": "messaging-and-background-jobs",
          "source": "db"
        },
        "dimension_id": 291,
        "input_skill": "Kafka",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "PHP Backend Developer",
            "id": 86,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "php-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Ruby Backend Developer",
            "id": 85,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "ruby-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 36,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Messaging and Event Streaming",
          "id": 8,
          "rationale": "Transport-layer systems used to move events and decouple producers from consumers. Data engineers use these systems to ingest, buffer, and distribute event data before downstream processing.",
          "slug": "messaging-and-event-streaming",
          "source": "db"
        },
        "dimension_id": 8,
        "input_skill": "Kafka",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 36,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Scripting \u0026 DSL Languages",
          "id": 248,
          "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
          "slug": "cloud-security-scripting-dsl-languages",
          "source": "db"
        },
        "dimension_id": 248,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
          "slug": "programming-languages",
          "source": "db"
        },
        "dimension_id": 1,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 435,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "fullstack-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages and Scripting",
          "id": 59,
          "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
          "slug": "programming-languages-and-scripting",
          "source": "db"
        },
        "dimension_id": 59,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
          "id": 39,
          "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 39,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for XR",
          "id": 97,
          "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
          "slug": "programming-languages-for-xr",
          "source": "db"
        },
        "dimension_id": 97,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "AR/VR Engineer",
            "id": 8,
            "rationale": null,
            "role_archetype": null,
            "slug": "ar-vr-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Python Programming",
          "id": 290,
          "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
          "slug": "python-programming",
          "source": "db"
        },
        "dimension_id": 290,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "dbt",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 115,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Spark",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1350,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Backend Observability, Logging, and Diagnostics",
          "id": 388,
          "rationale": "Instrumentation and troubleshooting practices used to understand and improve backend service behavior in production and lower environments. This includes logs, metrics, traces, alerting, dashboards, structured logging, distributed tracing, health checks, and root-cause analysis using ecosystem-specific tools such as SLF4J, Logback, Micrometer, OpenTelemetry, Prometheus, Grafana, ILogger, Serilog, and Application Insights.",
          "slug": "backend-observability-logging-and-diagnostics",
          "source": "db"
        },
        "dimension_id": 388,
        "input_skill": "Grafana",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 47,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Observability and Incident Response",
          "id": 10,
          "rationale": "Instrumentation and production troubleshooting practices used to keep backend services reliable. Includes logs, metrics, traces, alerting, dashboards, and incident diagnosis.",
          "slug": "observability-and-incident-response",
          "source": "db"
        },
        "dimension_id": 10,
        "input_skill": "Grafana",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "PHP Backend Developer",
            "id": 86,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "php-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 47,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Observability and Incident Triage",
          "id": 155,
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LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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