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Pipeline run

e1d7cd36-0c7c-4be5-9ffc-ffd0d21bf053

Pipeline LLM cost (USD)
API 1: $0.0094 API 2: $0.0003 API 3: $0.0000 Total: $0.0097

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · Data Engineering / Streaming Platforms
Build and run streaming big-data platforms: design and test data solutions, manage Kafka/Flink/Druid/NoSQL clusters, and handle sizing, deployment, monitoring, scaling, security, and high availability. Also support ingestion, cleansing, ETL, and exposing data for consumers.
"Responsible for full operational lifecycle of streaming platform including cluster sizing, technology selection, deployment, monitoring, scaling & helping with kafka apps development."
Tech stack maturity
Modern Cloud Native
The skill set centers on containerized, orchestrated, cloud-era real-time data infrastructure (Kafka, Flink, Spark, Kubernetes, observability, and security controls), which is characteristic of a modern cloud-native stack.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.00 / 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):
Evidence — skills matched in JD (20)
Kafka Apache Flink Druid NoSQL Apache Spark Cloud Computing Docker Kubernetes Grafana Prometheus Elasticsearch Kibana Kerberos LDAP TLS RBAC ETL OpenShift Python Agile
Skill cluster (8 dimension groups, role-scoped)
Observability and Operations
Grafana Prometheus
Asynchronous Messaging and Event Streaming
Kafka
Authentication and Authorization
RBAC
Container Orchestration Platforms
Kubernetes
Containerization and Image Builds
Docker
Python Programming
Python
Search and Content Discovery
Elasticsearch
Cross-cutting / unaligned
Apache Flink Druid NoSQL Apache Spark Cloud Computing Kibana Kerberos LDAP TLS ETL OpenShift Agile
Show KRA description ↓
As Data Engineer, you will develop, maintain, evaluate and test big data solutions. You will be involved in the design of data solutions. You will manage the implementing and managing large scale cluster for streaming using technologies like kafka, flink, Druid, NoSQL DB etc. Responsible for full operational lifecycle of streaming platform including cluster sizing, technology selection, deployment, monitoring, scaling & helping with kafka apps development. Responsible for ensuring platform availability and zero downtime. Implement scalable solutions to meet the ever-increasing data volumes, using big data/cloud technologies Apache Spark, Kafka, any Cloud computing etc. Minimum 4+ years of experience in Big Data technologies. Experience of implementing and managing large scale cluster for streaming kafka, flink, Druid, NoSQL DB etc. Expertise in Contenerizing cluster services using Docker and Kubernetes (preferably using Openshift). Solid practical experience of Cluster Administration and Monitoring using Grafana, Prometheus, ElasticSearch, Kibana or similar technologies. Hands on experience on securing clusters - Kerberos, LDAP, TLS, RBAC. Demonstrated ability in solutioning covering data ingestion, data cleansing, ETL, data mart creation and exposing data for consumers. Experience in Python programming is desirable. You love collaborative environments that use agile methodologies to encourage creative design thinking and find innovative ways to develop with cutting edge technologies. Ambitious individual who can work under their own direction towards agreed targets/goals and with creative approach to work. Intuitive individual with an ability to manage change and proven time management. Proven interpersonal skills while contributing to team effort by accomplishing related results as needed. Up-to-date technical knowledge by attending educational workshops, reviewing publications.

Signals

Skill backend-engineer
0.35
Alias data-engineer
1.00
KRA data-engineer
0.63

Post-classification

Centroidupdated · n=2
Alias collision log
New-role queue
New skills captured6
New KRA capturedyes

Captured for admin review

Druid primary Streaming / Real-Time Data Engineer pending
Cloud Computing primary Streaming / Real-Time Data Engineer pending
OpenShift Streaming / Real-Time Data Engineer pending
Kibana primary Streaming / Real-Time Data Engineer pending
Kerberos primary Streaming / Real-Time Data Engineer pending
ETL primary Streaming / Real-Time Data Engineer pending
R&R fragment (sim 0.00) Streaming / Real-Time Data Engineer pending

As Data Engineer, you will develop, maintain, evaluate and test big data solutions. You will be involved in the design of data solutions. You will manage the implementing and managing large scale clus…

Status: completed Created: 2026-05-27T13:59:32.082703Z Updated: 2026-05-27T14:01:34.694815Z API 3 duration: 69952 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

Streaming / Real-Time Data Engineer

domain · Data Engineering & Analytics CASE DOMAIN

slug: streaming-real-time-data-engineer · id: 149 · source: db

Domain=Data Engineering & Analytics; The JD centers on building and operating a streaming platform with Kafka, Flink, cluster management, monitoring, and zero-downtime availability, which best matches a streaming/real-time data engineering role.

Matched skills

big data solutionsKafkaFlinkDruidNoSQL DBApache SparkDockerKubernetesOpenShiftGrafanaPrometheusElasticSearchKibanaKerberosLDAPTLSRBACPython

Matched dimensions

Streaming platform engineeringLarge-scale cluster administrationData ingestion and ETLPlatform reliability and monitoringCloud-native deployment and containerizationSecurity and access control for clustersScalable big data processing

Matched KRAs

develop, maintain, evaluate and test big data solutionsdesign of data solutionsmanage the implementing and managing large scale cluster for streamingresponsible for full operational lifecycle of streaming platformcluster sizing, technology selection, deployment, monitoring, scalinghelping with kafka apps developmentensuring platform availability and zero downtimeimplement scalable solutions to meet ever-increasing data volumessolutioning covering data ingestion, data cleansing, ETL, data mart creationexposing data for consumers

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
0
Skipped

Job description

Introduction At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. Are you ready to lead in this new era of technology and solve some of the world's most challenging problems? If so, lets talk. Your Role and Responsibilities As Data Engineer, you will develop, maintain, evaluate and test big data solutions. You will be involved in the design of data solutions. You will manage the implementing and managing large scale cluster for streaming using technologies like kafka, flink, Druid, NoSQL DB etc. Responsibilities Responsible for full operational lifecycle of streaming platform including cluster sizing, technology selection, deployment, monitoring, scaling & helping with kafka apps development Responsible for ensuring platform availability and zero downtime Implement scalable solutions to meet the ever-increasing data volumes, using big data/cloud technologies Apache Spark, Kafka, any Cloud computing etc. If you thrive in a dynamic, collaborative workplace, IBM provides an environment where you will be challenged and inspired every single day. And if you relish the freedom to bring creative, thoughtful solutions to the table, there's no limit to what you can accomplish here. #GBSISACBDS2021 Required Technical and Professional Expertise Minimum 4+ years of experience in Big Data technologies  Experience of implementing and managing large scale cluster for streaming kafka, flink, Druid, NoSQL DB etc Expertise in Contenerizing cluster services using Docker and Kubernetes (preferably using Openshift) Solid practical experience of Cluster Administration and Monitoring using Grafana, Prometheus, ElasticSearch, Kibana or similar technologies Hands on experience on securing clusters - Kerberos, LDAP, TLS, RBAC Demonstrated ability in solutioning covering data ingestion, data cleansing, ETL, data mart creation and exposing data for consumers Preferred Technical And Professional Expertise Experience in Python programming is desirable You love collaborative environments that use agile methodologies to encourage creative design thinking and find innovative ways to develop with cutting edge technologies Ambitious individual who can work under their own direction towards agreed targets/goals and with creative approach to work Intuitive individual with an ability to manage change and proven time management Proven interpersonal skills while contributing to team effort by accomplishing related results as needed Up-to-date technical knowledge by attending educational workshops, reviewing publications NA About Business Unit IBM Services is a team of business, strategy and technology consultants that design, build, and run foundational systems and services that is the backbone of the world's economy. IBM Services partners with the world's leading companies in over 170 countries to build smarter businesses by reimagining and reinventing through technology, with its outcome-focused methodologies, industry-leading portfolio and world class research and operations expertise leading to results-driven innovation and enduring excellence. Your Life @ IBM What matters to you when you’re looking for your next career challenge? Maybe you want to get involved in work that really changes the world? What about somewhere with incredible and diverse career and development opportunities – where you can truly discover your passion? Are you looking for a culture of openness, collaboration and trust – where everyone has a voice? What about all of these? If so, then IBM could be your next career challenge. Join us, not to do something better, but to attempt things you never thought possible. Impact. Inclusion. Infinite Experiences. Do your best work ever. About IBM IBM’s greatest invention is the IBMer. We believe that progress is made through progressive thinking, progressive leadership, progressive policy and progressive action. IBMers believe that the application of intelligence, reason and science can improve business, society and the human condition. Restlessly reinventing since 1911, we are the largest technology and consulting employer in the world, with more than 380,000 IBMers serving clients in 170 countries. Location Statement For additional information about location requirements, please discuss with the recruiter following submission of your application. Being You @ IBM IBM is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, pregnancy, disability, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

Skills from this JD

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

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 skipped (dimension not under chosen role)
Apache Flink Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Flink id=120 · apache-flink

Aliases — catalog

  • Apache Flink (CANONICAL) primary
  • Apache Flink 1.20 (VERSION)
  • Apache Flink 1.x (VERSION)
  • Flink 1.20 (VERSION)
  • Flink 1.x (VERSION)

Context tags (catalog)

CEP DataStream API Flink SQL Kafka Kinesis SQL Table API checkpointing event time exactly-once state backend stateful processing stream processing watermarks windowing

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Stream Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2014
Confidence
0.95
Version strategy
SEPARATE_ENTITY
Version tag
1.20

Maturity reasoning: Apache Flink appears in streaming/data-platform JDs, but far less often than Spark/Kafka; GitHub and job-market signals show a specialized real-time processing niche rather than broad hiring staple.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
94
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Stream Processing Systems Catalog dimension db id 25

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Stream Processing Systems
stream-processing-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Druid 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
NoSQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: NoSQL id=1346 · nosql

Aliases — catalog

  • NoSQL (CANONICAL)

Context tags (catalog)

CAP theorem Cassandra DynamoDB MongoDB Redis column-family data modeling document store eventual consistency graph database horizontal scaling key-value store query language schema-less sharding

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Database Paradigm
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: NoSQL is broadly listed in job descriptions across backend/data roles, with MongoDB, DynamoDB, and Cassandra appearing as common market signals; it remains a hiring-pipeline staple rather than a niche or sunset tech.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1019
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • NoSQL Databases Catalog dimension db id 19

    Library dimension (catalog)

    Roles linked in library: Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
NoSQL Databases
nosql-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache 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 skipped (dimension not under chosen role)
Cloud Computing 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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Docker Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Docker id=61 · docker

Aliases — catalog

  • Docker (CANONICAL) primary

Context tags (catalog)

CI/CD Compose DevOps Docker Compose Docker Swarm Dockerfile Kubernetes build pipeline container container lifecycle container orchestration container registry container security containerization containers image image registry images immutable infrastructure lightweight virtualization microservices networking orchestration port mapping registry scalability service discovery swarm volume volume management

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Containerization Tool
Vendor
Docker, Inc.
License
apache_2
Year introduced
2013
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Docker is a hiring-pipeline staple: it appears in many DevOps, backend, and platform JDs, and remains a standard containerization tool alongside Kubernetes in production stacks.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
63
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Containerization and Image Builds Catalog dimension db id 152

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • Deployment and Cloud Platforms Catalog dimension db id 418

    Library dimension (catalog)

    Roles linked in library: Ruby Backend Developer

  • Deployment and Runtime Configuration Catalog dimension db id 13

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Go Backend Developer, PHP Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Containerization and Image Builds
containerization-and-image-builds
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Deployment and Cloud Platforms
deployment-and-cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Deployment and Runtime Configuration
deployment-and-runtime-configuration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubernetes Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Kubernetes id=726 · kubernetes

Aliases — catalog

  • Kubernetes (CANONICAL) primary
  • Kubernetes 1.0+ (VERSION)
  • Kubernetes 1.x (VERSION)
  • Kubernetes v1 (VERSION)
  • k8s (VERSION)
  • kubernetes 1.x (VERSION)
  • kubernetes latest (VERSION)

Context tags (catalog)

CI/CD Cluster Autoscaler ConfigMap DaemonSet Deployment Docker Grafana Helm Ingress Istio K8s Kubelet Namespace Pod Prometheus RBAC Secret Service StatefulSet containerization deployment etcd kubectl load balancing microservices namespace orchestration persistent storage scalability service mesh

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Container Orchestration Platform
Vendor
Cloud Native Computing Foundation
License
apache_2
Year introduced
2014
Confidence
0.90
Version strategy
SEPARATE_ENTITY
Version tag
1.30

Maturity reasoning: Broadly adopted in cloud-native stacks; Kubernetes appears in a large share of DevOps/SRE job descriptions and is the default orchestration platform across major cloud vendors.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
557
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Container Orchestration Platforms Catalog dimension db id 134

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, DevOps Engineer

  • Kubernetes for ML Workloads Catalog dimension db id 47

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Container Orchestration Platforms
container-orchestration-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubernetes for ML Workloads
kubernetes-for-ml-workloads
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
OpenShift Secondary 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
Container Orchestration
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
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)
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)
Elasticsearch Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Elasticsearch id=3171 · elasticsearch

Aliases — catalog

  • Elasticsearch (CANONICAL) primary
  • ES (VERSION)
  • ElasticSearch (VERSION)
  • Elasticsearch 6 (VERSION)
  • Elasticsearch 6.x (VERSION)
  • Elasticsearch 7 (VERSION)
  • Elasticsearch 7.x (VERSION)
  • Elasticsearch 8 (VERSION)
  • Elasticsearch 8.x (VERSION)
  • Elasticsearch v6 (VERSION)
  • Elasticsearch v7 (VERSION)
  • Elasticsearch v8 (VERSION)

Context tags (catalog)

ELK stack JSON Kibana Logstash Lucene REST API RESTful API aggregation cluster management data visualization document store full-text search indexing performance tuning query DSL real-time search replication scalability search analytics search relevance sharding

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Search Datastore
Vendor
Elastic NV
License
apache_2
Year introduced
2010
Confidence
0.93
Version strategy
SEPARATE_ENTITY
Version tag
8.x

Maturity reasoning: Commonly listed in job descriptions for search/log analytics roles and widely deployed in production; Elastic’s docs and ecosystem show sustained adoption rather than sunset or replacement.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
2925
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Magento Search and Indexing Catalog dimension db id 403

    Library dimension (catalog)

    Roles linked in library: Magento Dev

  • Search and Content Discovery Catalog dimension db id 356

    Library dimension (catalog)

    Roles linked in library: Drupal Dev, Sitecore Dev

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Magento Search and Indexing
magento-search-and-indexing
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Search and Content Discovery
search-and-content-discovery
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kibana 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
Monitoring Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Kerberos 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
Security Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
LDAP Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: LDAP id=3143 · ldap

Aliases — catalog

  • LDAP (CANONICAL) primary

Context tags (catalog)

Active Directory DN LDAP filter LDAP query LDAP server LDAPv3 SSL/TLS attributes authentication bind directory service entry replication schema search base

Stored enrichment (catalog DB)

Category
Protocol
Sub-category
Directory Access Protocol
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: LDAP remains a common enterprise directory protocol; many JDs still list it for identity, SSO, and directory integration work, especially alongside Active Directory and OpenLDAP.

Skill profile (library / DB)

Skill nature
PROTOCOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
10
Sub-category id
2450
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Authentication and Identity Integration Catalog dimension db id 351

    Library dimension (catalog)

    Roles linked in library: Drupal Dev

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Authentication and Identity Integration
authentication-and-identity-integration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
TLS Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: TLS id=306 · tls

Aliases — catalog

  • TLS (CANONICAL) primary

Context tags (catalog)

ALPN CA CRL HTTPS OCSP PKI SNI SSL TLS 1.2 TLS 1.3 X.509 certificate certificate chain cipher suite cipher suites data integrity encryption handshake mTLS mutual authentication secure communication secure sockets session resumption

Stored enrichment (catalog DB)

Category
Protocol
Sub-category
Transport Security Protocol
Vendor
IETF
License
other_open
Year introduced
1999
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: TLS is a standard requirement in most security, backend, and cloud job descriptions; it underpins HTTPS and is widely supported by major vendors and browsers, with no announced sunset or replacement.

Skill profile (library / DB)

Skill nature
PROTOCOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
10
Sub-category id
1374
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cryptography and PKI Catalog dimension db id 67

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer, Cyber Security Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cryptography and PKI
cryptography-and-pki
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
RBAC Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: RBAC id=29 · rbac

Aliases — catalog

  • RBAC (CANONICAL) primary

Context tags (catalog)

ACL IAM LDAP OAuth SAML access control access matrix access policies attribute-based access attribute-based access control audit trails authorization compliance entitlements group membership identity management least privilege multi-factor authentication multi-tenancy permissions policy policy enforcement principle of least privilege privilege escalation privileges resource access role hierarchy role-based access control roles security model security policies segregation of duties user management user roles

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Access Control Concept
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: RBAC is a standard access-control model widely listed in security and IAM job descriptions, and it underpins common products like AWS IAM and Kubernetes RBAC.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
5
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Authentication and Authorization Catalog dimension db id 6

    Library dimension (catalog)

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

  • Backend Authentication and Authorization Catalog dimension db id 386

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Authentication and Authorization
authentication-and-authorization
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Backend Authentication and Authorization
backend-authentication-and-authorization
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
ETL 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
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Python Secondary 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

  • 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 skipped (dimension not under chosen role)
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)
Agile Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Agile id=520 · agile

Aliases — catalog

  • Agile (CANONICAL) primary

Context tags (catalog)

Kanban SAFe Scrum backlog backlog grooming burndown burndown chart continuous delivery continuous improvement cross-functional daily standup epics incremental development iteration iteration planning lean product backlog product owner retrospective sprint sprint planning stand-up story points user stories velocity

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Agile
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Agile appears in a large share of software job descriptions and is a standard hiring-pipeline requirement; Scrum/Kanban are commonly listed alongside it, showing broad market adoption.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
367
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)

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 skipped (dimension not under chosen role)
Apache Flink in_db
Stream Processing Systems
stream-processing-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
NoSQL in_db
NoSQL Databases
nosql-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker in_db
Containerization and Image Builds
containerization-and-image-builds
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker in_db
Deployment and Cloud Platforms
deployment-and-cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker in_db
Deployment and Runtime Configuration
deployment-and-runtime-configuration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubernetes in_db
Container Orchestration Platforms
container-orchestration-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubernetes in_db
Kubernetes for ML Workloads
kubernetes-for-ml-workloads
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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)
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)
Elasticsearch in_db
Magento Search and Indexing
magento-search-and-indexing
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Elasticsearch in_db
Search and Content Discovery
search-and-content-discovery
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
LDAP in_db
Authentication and Identity Integration
authentication-and-identity-integration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
TLS in_db
Cryptography and PKI
cryptography-and-pki
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
RBAC in_db
Authentication and Authorization
authentication-and-authorization
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
RBAC in_db
Backend Authentication and Authorization
backend-authentication-and-authorization
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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 skipped (dimension not under chosen role)
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)
Agile in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Druid | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Cloud Computing | type=Concepts subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed OpenShift | type=Container Orchestration subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Kibana | type=Monitoring Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Kerberos | type=Security Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
nano JD Parser — gpt-4.1-nano click to toggle
RoleData Engineer
CompanyIBM
ExperienceMinimum 4+ years of experience in Big Data technologies
DomainIT Services & Consulting
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "IBM\u2019s greatest invention is the",
      "last_5_words": "clients in 170 countries."
    },
    "text": "IBM\u2019s greatest invention is the IBMer. We believe that progress is made through progressive thinking, progressive leadership, progressive policy and progressive action. IBMers believe that the application of intelligence, reason and science can improve business, society and the human condition. Restlessly reinventing since 1911, we are the largest technology and consulting employer in the world, with more than 380,000 IBMers serving clients in 170 countries.",
    "word_count": 64
  },
  "certifications": [],
  "company_name": "IBM",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "ITES",
        "BPO",
        "Tech Consulting"
      ],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": 4,
    "raw": "Minimum 4+ years of experience in Big Data technologies"
  },
  "job_locations": [],
  "role": "Data Engineer",
  "role_aliases": [
    "Big Data Engineer",
    "Data Platform Engineer",
    "Data Solutions Engineer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 0,
      "heading": "Your Role and Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "As Data Engineer, you will",
        "last_5_words": "Druid, NoSQL DB etc."
      },
      "text": "As Data Engineer, you will develop, maintain, evaluate and test big data solutions. You will be involved in the design of data solutions. You will manage the implementing and managing large scale cluster for streaming using technologies like kafka, flink, Druid, NoSQL DB etc.",
      "word_count": 45
    },
    {
      "bullet_count": 3,
      "heading": "Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Responsible for full operational lifecycle",
        "last_5_words": "big data/cloud technologies Apache Spark"
      },
      "text": "Responsible for full operational lifecycle of streaming platform including cluster sizing, technology selection, deployment, monitoring, scaling \u0026 helping with kafka apps development.\nResponsible for ensuring platform availability and zero downtime.\nImplement scalable solutions to meet the ever-increasing data volumes, using big data/cloud technologies Apache Spark, Kafka, any Cloud computing etc.",
      "word_count": 54
    },
    {
      "bullet_count": 6,
      "heading": "Required Technical and Professional Expertise",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Minimum 4+ years of experience",
        "last_5_words": "data mart creation and exposing"
      },
      "text": "Minimum 4+ years of experience in Big Data technologies.\nExperience of implementing and managing large scale cluster for streaming kafka, flink, Druid, NoSQL DB etc.\nExpertise in Contenerizing cluster services using Docker and Kubernetes (preferably using Openshift).\nSolid practical experience of Cluster Administration and Monitoring using Grafana, Prometheus, ElasticSearch, Kibana or similar technologies.\nHands on experience on securing clusters - Kerberos, LDAP, TLS, RBAC.\nDemonstrated ability in solutioning covering data ingestion, data cleansing, ETL, data mart creation and exposing data for consumers.",
      "word_count": 83
    },
    {
      "bullet_count": 6,
      "heading": "Preferred Technical And Professional Expertise",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Experience in Python programming is",
        "last_5_words": "attending educational workshops, reviewing"
      },
      "text": "Experience in Python programming is desirable.\nYou love collaborative environments that use agile methodologies to encourage creative design thinking and find innovative ways to develop with cutting edge technologies.\nAmbitious individual who can work under their own direction towards agreed targets/goals and with creative approach to work.\nIntuitive individual with an ability to manage change and proven time management.\nProven interpersonal skills while contributing to team effort by accomplishing related results as needed.\nUp-to-date technical knowledge by attending educational workshops, reviewing publications.",
      "word_count": 83
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Kafka"
    },
    {
      "is_primary": true,
      "skill_name": "Apache Flink"
    },
    {
      "is_primary": true,
      "skill_name": "Druid"
    },
    {
      "is_primary": true,
      "skill_name": "NoSQL"
    },
    {
      "is_primary": true,
      "skill_name": "Apache Spark"
    },
    {
      "is_primary": true,
      "skill_name": "Cloud Computing"
    },
    {
      "is_primary": true,
      "skill_name": "Docker"
    },
    {
      "is_primary": true,
      "skill_name": "Kubernetes"
    },
    {
      "is_primary": false,
      "skill_name": "OpenShift"
    },
    {
      "is_primary": true,
      "skill_name": "Grafana"
    },
    {
      "is_primary": true,
      "skill_name": "Prometheus"
    },
    {
      "is_primary": true,
      "skill_name": "Elasticsearch"
    },
    {
      "is_primary": true,
      "skill_name": "Kibana"
    },
    {
      "is_primary": true,
      "skill_name": "Kerberos"
    },
    {
      "is_primary": true,
      "skill_name": "LDAP"
    },
    {
      "is_primary": true,
      "skill_name": "TLS"
    },
    {
      "is_primary": true,
      "skill_name": "RBAC"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": false,
      "skill_name": "Python"
    },
    {
      "is_primary": false,
      "skill_name": "Agile"
    }
  ],
  "jd_role": {
    "display_name": "Data Engineer",
    "rationale": null,
    "role_aliases": [
      "Big Data Engineer",
      "Data Platform Engineer",
      "Data Solutions Engineer"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "IBM\u2019s greatest invention is the",
        "last_5_words": "clients in 170 countries."
      },
      "text": "IBM\u2019s greatest invention is the IBMer. We believe that progress is made through progressive thinking, progressive leadership, progressive policy and progressive action. IBMers believe that the application of intelligence, reason and science can improve business, society and the human condition. Restlessly reinventing since 1911, we are the largest technology and consulting employer in the world, with more than 380,000 IBMers serving clients in 170 countries.",
      "word_count": 64
    },
    "certifications": [],
    "company_name": "IBM",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "ITES",
          "BPO",
          "Tech Consulting"
        ],
        "domain": "IT Services \u0026 Consulting"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": null,
      "min": 4,
      "raw": "Minimum 4+ years of experience in Big Data technologies"
    },
    "job_locations": [],
    "role": "Data Engineer",
    "role_aliases": [
      "Big Data Engineer",
      "Data Platform Engineer",
      "Data Solutions Engineer"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 0,
        "heading": "Your Role and Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "As Data Engineer, you will",
          "last_5_words": "Druid, NoSQL DB etc."
        },
        "text": "As Data Engineer, you will develop, maintain, evaluate and test big data solutions. You will be involved in the design of data solutions. You will manage the implementing and managing large scale cluster for streaming using technologies like kafka, flink, Druid, NoSQL DB etc.",
        "word_count": 45
      },
      {
        "bullet_count": 3,
        "heading": "Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Responsible for full operational lifecycle",
          "last_5_words": "big data/cloud technologies Apache Spark"
        },
        "text": "Responsible for full operational lifecycle of streaming platform including cluster sizing, technology selection, deployment, monitoring, scaling \u0026 helping with kafka apps development.\nResponsible for ensuring platform availability and zero downtime.\nImplement scalable solutions to meet the ever-increasing data volumes, using big data/cloud technologies Apache Spark, Kafka, any Cloud computing etc.",
        "word_count": 54
      },
      {
        "bullet_count": 6,
        "heading": "Required Technical and Professional Expertise",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Minimum 4+ years of experience",
          "last_5_words": "data mart creation and exposing"
        },
        "text": "Minimum 4+ years of experience in Big Data technologies.\nExperience of implementing and managing large scale cluster for streaming kafka, flink, Druid, NoSQL DB etc.\nExpertise in Contenerizing cluster services using Docker and Kubernetes (preferably using Openshift).\nSolid practical experience of Cluster Administration and Monitoring using Grafana, Prometheus, ElasticSearch, Kibana or similar technologies.\nHands on experience on securing clusters - Kerberos, LDAP, TLS, RBAC.\nDemonstrated ability in solutioning covering data ingestion, data cleansing, ETL, data mart creation and exposing data for consumers.",
        "word_count": 83
      },
      {
        "bullet_count": 6,
        "heading": "Preferred Technical And Professional Expertise",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Experience in Python programming is",
          "last_5_words": "attending educational workshops, reviewing"
        },
        "text": "Experience in Python programming is desirable.\nYou love collaborative environments that use agile methodologies to encourage creative design thinking and find innovative ways to develop with cutting edge technologies.\nAmbitious individual who can work under their own direction towards agreed targets/goals and with creative approach to work.\nIntuitive individual with an ability to manage change and proven time management.\nProven interpersonal skills while contributing to team effort by accomplishing related results as needed.\nUp-to-date technical knowledge by attending educational workshops, reviewing publications.",
        "word_count": 83
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "e1d7cd36-0c7c-4be5-9ffc-ffd0d21bf053",
  "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
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}
API 2 — extract-details
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    },
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        "display_name": "Search and Content Discovery",
        "id": 356,
        "rationale": "Implementing site search, indexing, and content discovery features in Drupal. This cluster is coherent because many Drupal sites need structured search experiences beyond basic navigation.",
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        "source": "db"
      },
      "input_skill": "Elasticsearch",
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      "roles_from_db": [
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          "display_name": "Drupal Dev",
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          "rationale": null,
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        {
          "display_name": "Sitecore Dev",
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    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Authentication and Identity Integration",
        "id": 351,
        "rationale": "Sign-in and identity-related integration work for Drupal sites, including external identity providers and session-related behavior. This is distinct from general permissions because it focuses on how users authenticate and federate into the site.",
        "slug": "authentication-and-identity-integration",
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      "input_skill": "LDAP",
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          "input_skill": "Elasticsearch",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Drupal Dev",
              "id": 228,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "drupal-dev",
              "source": "db"
            },
            {
              "display_name": "Sitecore Dev",
              "id": 233,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "sitecore-dev",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Elasticsearch",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Kibana",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Monitoring Tools",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "kibana",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Kerberos",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Security Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "kerberos",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "LDAP",
          "alias_type": "CANONICAL",
          "id": 4620,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 10,
        "display_name": "LDAP",
        "id": 3143,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PROTOCOL",
        "slug": "ldap",
        "sub_category_id": 2450,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Authentication and Identity Integration",
            "id": 351,
            "rationale": "Sign-in and identity-related integration work for Drupal sites, including external identity providers and session-related behavior. This is distinct from general permissions because it focuses on how users authenticate and federate into the site.",
            "slug": "authentication-and-identity-integration",
            "source": "db"
          },
          "input_skill": "LDAP",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Drupal Dev",
              "id": 228,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "drupal-dev",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "LDAP",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "TLS",
          "alias_type": "CANONICAL",
          "id": 622,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 10,
        "display_name": "TLS",
        "id": 306,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PROTOCOL",
        "slug": "tls",
        "sub_category_id": 1374,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cryptography and PKI",
            "id": 67,
            "rationale": "Cryptographic primitives and trust infrastructure used to protect data, identities, and communications. This is a coherent cluster because the role needs to reason about keys, certificates, signatures, and protocol internals when reviewing controls.",
            "slug": "cryptography-and-pki",
            "source": "db"
          },
          "input_skill": "TLS",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Security Engineer",
              "id": 23,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-security-engineer",
              "source": "db"
            },
            {
              "display_name": "Cyber Security Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "TLS",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "RBAC",
          "alias_type": "CANONICAL",
          "id": 166,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "RBAC",
        "id": 29,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "rbac",
        "sub_category_id": 5,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Authentication and Authorization",
            "id": 6,
            "rationale": "Identity, session, and access-control mechanisms used to protect PHP backend resources. This includes login flows, token handling, role checks, permissions, and policy enforcement in server-side code.",
            "slug": "authentication-and-authorization",
            "source": "db"
          },
          "input_skill": "RBAC",
          "llm_role": null,
          "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": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Java Backend Developer",
              "id": 79,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "java-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": "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"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Backend Authentication and Authorization",
            "id": 386,
            "rationale": "Identity and access control mechanisms used by backend services to authenticate users or clients and enforce permissions on protected resources. This includes token and session handling, OAuth 2.0 / OpenID Connect / SAML flows, JWT and access tokens, API keys, MFA, role- or claim-based authorization, RBAC/ABAC, scopes, policies, Spring Security, and authorization filters.",
            "slug": "backend-authentication-and-authorization",
            "source": "db"
          },
          "input_skill": "RBAC",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "RBAC",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "ETL",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "etl",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Python",
          "alias_type": "CANONICAL",
          "id": 67,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 2",
          "alias_type": "VERSION",
          "id": 72,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 2.x",
          "alias_type": "VERSION",
          "id": 74,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3",
          "alias_type": "VERSION",
          "id": 73,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3.10",
          "alias_type": "VERSION",
          "id": 76,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3.11",
          "alias_type": "VERSION",
          "id": 77,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3.12",
          "alias_type": "VERSION",
          "id": 78,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3.x",
          "alias_type": "VERSION",
          "id": 75,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "py",
          "alias_type": "VERSION",
          "id": 2183,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "py2",
          "alias_type": "VERSION",
          "id": 68,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "py3",
          "alias_type": "VERSION",
          "id": 69,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python 3",
          "alias_type": "VERSION",
          "id": 2186,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python 3.x",
          "alias_type": "VERSION",
          "id": 2849,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python2",
          "alias_type": "VERSION",
          "id": 70,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python3",
          "alias_type": "VERSION",
          "id": 71,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python3.x",
          "alias_type": "VERSION",
          "id": 2848,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "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"
      },
      "dimensions": [
        {
          "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",
              "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"
            }
          ]
        },
        {
          "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": [
            {
              "display_name": "Cyber Security Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "source": "db"
            }
          ]
        },
        {
          "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": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Python",
          "llm_role": null,
          "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"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AR/VR Engineer",
              "id": 8,
              "rationale": null,
              "role_archetype": null,
              "slug": "ar-vr-engineer",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Python",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Agile",
          "alias_type": "CANONICAL",
          "id": 868,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "Agile",
        "id": 520,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "agile",
        "sub_category_id": 367,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Agile",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Agile",
      "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": [
    "Druid",
    "Cloud Computing",
    "OpenShift",
    "Kibana",
    "Kerberos",
    "ETL"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Streaming / Real-Time Data Engineer",
    "id": 149,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on building and operating a streaming platform with Kafka, Flink, cluster management, monitoring, and zero-downtime availability, which best matches a streaming/real-time data engineering role.",
    "role_archetype": null,
    "slug": "streaming-real-time-data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Kafka",
      "tag": "in_db"
    },
    {
      "skill": "Apache Flink",
      "tag": "in_db"
    },
    {
      "skill": "Druid",
      "tag": "new"
    },
    {
      "skill": "NoSQL",
      "tag": "in_db"
    },
    {
      "skill": "Apache Spark",
      "tag": "in_db"
    },
    {
      "skill": "Cloud Computing",
      "tag": "new"
    },
    {
      "skill": "Docker",
      "tag": "in_db"
    },
    {
      "skill": "Kubernetes",
      "tag": "in_db"
    },
    {
      "skill": "OpenShift",
      "tag": "new"
    },
    {
      "skill": "Grafana",
      "tag": "in_db"
    },
    {
      "skill": "Prometheus",
      "tag": "in_db"
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          {
            "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": 46,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 149,
        "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"
        },
        "dimension_id": 155,
        "input_skill": "Prometheus",
        "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": "DevOps Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "devops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 46,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 149,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Observability and Operations",
          "id": 143,
          "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"
        },
        "dimension_id": 143,
        "input_skill": "Prometheus",
        "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 Architect",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 46,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 149,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Magento Search and Indexing",
          "id": 403,
          "rationale": "Index-driven storefront data freshness and search behavior. This cluster is coherent because Magento commerce sites depend on indexing to keep catalog, price, and search results aligned with changing data.",
          "slug": "magento-search-and-indexing",
          "source": "db"
        },
        "dimension_id": 403,
        "input_skill": "Elasticsearch",
        "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": "Magento Dev",
            "id": 231,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "magento-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 3171,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 149,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Search and Content Discovery",
          "id": 356,
          "rationale": "Implementing site search, indexing, and content discovery features in Drupal. This cluster is coherent because many Drupal sites need structured search experiences beyond basic navigation.",
          "slug": "search-and-content-discovery",
          "source": "db"
        },
        "dimension_id": 356,
        "input_skill": "Elasticsearch",
        "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": "Drupal Dev",
            "id": 228,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "drupal-dev",
            "source": "db"
          },
          {
            "display_name": "Sitecore Dev",
            "id": 233,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "sitecore-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 3171,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 149,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Authentication and Identity Integration",
          "id": 351,
          "rationale": "Sign-in and identity-related integration work for Drupal sites, including external identity providers and session-related behavior. This is distinct from general permissions because it focuses on how users authenticate and federate into the site.",
          "slug": "authentication-and-identity-integration",
          "source": "db"
        },
        "dimension_id": 351,
        "input_skill": "LDAP",
        "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": "Drupal Dev",
            "id": 228,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "drupal-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 3143,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 149,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cryptography and PKI",
          "id": 67,
          "rationale": "Cryptographic primitives and trust infrastructure used to protect data, identities, and communications. This is a coherent cluster because the role needs to reason about keys, certificates, signatures, and protocol internals when reviewing controls.",
          "slug": "cryptography-and-pki",
          "source": "db"
        },
        "dimension_id": 67,
        "input_skill": "TLS",
        "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"
          },
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 306,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 149,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Authentication and Authorization",
          "id": 6,
          "rationale": "Identity, session, and access-control mechanisms used to protect PHP backend resources. This includes login flows, token handling, role checks, permissions, and policy enforcement in server-side code.",
          "slug": "authentication-and-authorization",
          "source": "db"
        },
        "dimension_id": 6,
        "input_skill": "RBAC",
        "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,
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            "source": "db"
          },
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
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            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Go Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "go-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Java Backend Developer",
            "id": 79,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "java-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": "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"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 29,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 149,
        "dimension": {
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          "display_name": "Backend Authentication and Authorization",
          "id": 386,
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          "slug": "backend-authentication-and-authorization",
          "source": "db"
        },
        "dimension_id": 386,
        "input_skill": "RBAC",
        "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",
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            "rationale": null,
            "role_archetype": "Engineering",
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            "source": "db"
          },
          {
            "display_name": "Ruby Backend Developer",
            "id": 85,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "ruby-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
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            "rationale": null,
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            "source": "db"
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        ],
        "skill_dimension_saved": true,
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        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 149,
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          "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,
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            "slug": "cloud-security-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 149,
        "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"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 149,
        "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",
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 149,
        "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": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "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,
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        "skipped_reason": null
      },
      {
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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.",
          "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",
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            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
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          "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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        },
        "dimension_id": 97,
        "input_skill": "Python",
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        "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,
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        "skipped_reason": null
      },
      {
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          "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": 149,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "React Frontend Development",
          "id": 96,
          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 96,
        "input_skill": "Agile",
        "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": [],
        "skill_dimension_saved": true,
        "skill_id": 520,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 0
  },
  "planner_output": null,
  "run_id": "e1d7cd36-0c7c-4be5-9ffc-ffd0d21bf053"
}

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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