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Pipeline run

8a2ffc73-8fe4-45b4-880f-080010e9e83d

Pipeline LLM cost (USD)
API 1: $0.0060 API 2: $0.0016 API 3: $0.0000 Total: $0.0077

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data transformation and modeling
Build and operate real-time and batch data pipelines, model bronze→silver→gold lakehouse layers, and tune SQL/OLAP performance while adding data quality, observability, and governance controls.
"Model data across bronze → silver → gold layers for downstream teams."
Tech stack maturity
Modern Cloud Native
The skill set centers on modern data orchestration, streaming, and lakehouse tooling (Airflow, Dagster, dbt, Kafka, Spark, parquet) that is characteristic of cloud-native data platform work.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.20 / 5
· Title match
Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3): AI
Evidence — skills matched in JD (50)
Kafka Pulsar Spark Apache Spark Apache Iceberg ClickHouse StarRocks Airflow dbt Dagster SQLMesh Trino SQL Python Parquet Distributed Systems OLAP ETL Data Pipelines Lakehouse Apache Flink Polaris Gravitino Java Cube.js +25
Skill cluster (14 dimension groups, role-scoped)
ETL and ELT Tooling
Spark Apache Spark dbt
Programming Languages for Data Work
SQL Python Java
Cloud Platforms
Distributed Systems
Container Orchestration Platforms
Kubernetes
Containerization and Image Builds
Docker
Data Pipeline Orchestration
Dagster
Data Quality and Reconciliation
Anomaly Detection
Data Serialization Standards & Protocols
Parquet
Infrastructure as Code
Terraform
Messaging and Event Streaming
Kafka
Performance and Cost Optimization
Query Optimization
Relational Database Design
Indexing
Stream Processing Systems
Apache Flink
Cross-cutting / unaligned
Pulsar Apache Iceberg ClickHouse StarRocks Airflow SQLMesh Trino OLAP ETL Data Pipelines Lakehouse Polaris Gravitino Cube.js OLTP Query Planning Window Functions Checkpointing Replay Logic Data Lineage Data Observability Semantic Layers Monitoring Containerization Encryption Access Control Distributed Execution Parallel Workloads Query Execution Storage Formats Big Data RisingWave Arroyo
Show KRA description ↓
• Build and maintain high-throughput, real-time data pipelines using Kafka/Pulsar with Spark, • Design fault-tolerant systems with zero-data-loss principles — checkpointing, replay logic, • Implement data observability — quality checks, SLA alerts, anomaly detection, lineage, and • Design and manage Iceberg-based lakehouse tables (Polaris/Gravitino catalogs, schema • Build fast OLAP layers using ClickHouse / StarRocks. • Model data across bronze → silver → gold layers for downstream teams. • Migrate and modernize legacy pipelines into scalable, distributed workflows. • Orchestrate ETL workloads using Airflow, DBT, Dagster, SQLMesh. • Optimize SQL transformations and distributed execution across Trino/Spark. • Ensure strict security and governance across all data layers — access control, encryption, • Collaborate with backend, analytics, and platform teams for seamless data delivery. • Extremely strong SQL — window functions, query planning, optimization. • High comfort working with distributed & parallel workloads. • Hands-on experience with some-many of these technologies : Apache Spark, Apache Flink, • Advanced experience in Python (preferred) or Java (strong fundamentals). • Strong understanding of Parquet, Apache Iceberg, and Iceberg REST catalogs (Polaris / • Experience with OLAP databases — ClickHouse / StarRocks. • Experience with semantic layers — Cube.js or similar. • Strong experience building pipelines with Airflow, DBT, Dagster, SQLMesh. • Solid understanding of data structures & algorithms — sorting, searching, memory models. • Strong grasp of OLTP vs OLAP, indexing, query execution, and storage formats. • Ability to debug distributed systems end-to-end (compute, storage, network, orchestration). • Familiarity with cloud environments, containerization (Docker), and monitoring. • Experience with large-scale data — high throughput, billions of rows, large parallel workloads. • Awareness of cost optimization in compute & storage. • Experience with emerging stream processors — Dagster, RisingWave, Arroyo. • Kubernetes, Terraform, or cloud-native big-data stacks. • Strong ownership — takes systems from design → build → monitor. • Self-driven, independent, and comfortable making technical decisions. • High attention to reliability, data accuracy, and operational excellence. • Naturally grows into broader technical responsibility as the platform scales.

Signals

Skill data-engineer
0.40
Alias data-engineer
1.00
KRA data-engineer
0.66

Post-classification

Centroidupdated · n=90
Alias collision log
New-role queue
New skills captured30
New KRA captured

Captured for admin review

Pulsar primary Data Engineer pending
Apache Iceberg primary Data Engineer pending
Polaris Data Engineer pending
Gravitino Data Engineer pending
ClickHouse primary Data Engineer pending
StarRocks primary Data Engineer pending
SQLMesh primary Data Engineer pending
Trino primary Data Engineer pending
Cube.js Data Engineer pending
OLTP Data Engineer pending
OLAP primary Data Engineer pending
Query Planning Data Engineer pending
Window Functions Data Engineer pending
Checkpointing Data Engineer pending
Replay Logic Data Engineer pending
Data Lineage Data Engineer pending
Data Observability Data Engineer pending
ETL primary Data Engineer pending
Data Pipelines primary Data Engineer pending
Semantic Layers Data Engineer pending
Containerization Data Engineer pending
Encryption Data Engineer pending
Access Control Data Engineer pending
Distributed Execution Data Engineer pending
Parallel Workloads Data Engineer pending
Query Execution Data Engineer pending
Storage Formats Data Engineer pending
Big Data Data Engineer pending
RisingWave Data Engineer pending
Arroyo Data Engineer pending
Status: extract_details_done Created: 2026-05-27T13:55:02.605903Z Updated: 2026-05-27T14:00:49.157697Z
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Data Engineer

CASE A

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

Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top data-engineer 0.40 does not contradict

Job description

Experience: 5.00 + years

Salary: Confidential (based on experience)

Shift: (GMT+05:30) Asia/Kolkata (IST)

Opportunity Type: Remote

Placement Type: Full time Permanent Position

(*Note: This is a requirement for one of Uplers' client - 1digitalstack.ai)

What do you need for this opportunity?

Must have skills required:

Python, Java, Iceberg, Kafka, Apache Beam, Apache Flink, Apache pulsar, Spark, Trino, OLAP, ClickHouse, starrocks

1digitalstack.ai is Looking for:

Role - Senior Data Engineer

Experience - 5-7 Years

Location - Remote (India)

About 1DigitalStack.ai

1DigitalStack.ai combines AI and deep eCommerce data to help global brands grow faster on online

marketplaces. Our platforms deliver advanced analytics, actionable intelligence, and media

automation — enabling brands to optimize visibility, efficiency, and sales performance at scale.

We partner with India’s top consumer companies — Unilever, Marico, Coca-Cola, Tata Consumer, Dabur,

and Unicharm — across 125+ marketplaces globally.

Backed by leading venture investors and powered by a 220+ member team, we’re in our $5–10M

growth journey, scaling rapidly across categories and geographies to redefine how brands win on

digital shelves.

🔗 Check out more at www.1digitalstack.ai

About Role

This is a high-impact, hands-on engineering role owning the core data systems that power our

analytics, AI, and automation stack.

You’ll work closely with the CTO and Engineering Leads and independently manage large,

high-throughput data pipelines that process millions of events.

Responsibilities :

• Build and maintain high-throughput, real-time data pipelines using Kafka/Pulsar with Spark,



Flink, and distributed compute engines.

• Design fault-tolerant systems with zero-data-loss principles — checkpointing, replay logic,



DLQs, deduplication, and back-pressure handling.

• Implement data observability — quality checks, SLA alerts, anomaly detection, lineage, and



metadata insights.

• Design and manage Iceberg-based lakehouse tables (Polaris/Gravitino catalogs, schema



evolution, compaction).

• Build fast OLAP layers using ClickHouse / StarRocks.
• Model data across bronze → silver → gold layers for downstream teams.
• Migrate and modernize legacy pipelines into scalable, distributed workflows.
• Orchestrate ETL workloads using Airflow, DBT, Dagster, SQLMesh.
• Optimize SQL transformations and distributed execution across Trino/Spark.
• Ensure strict security and governance across all data layers — access control, encryption,



auditability.

• Collaborate with backend, analytics, and platform teams for seamless data delivery.



Requirements

Core Technical Skills

• Extremely strong SQL — window functions, query planning, optimization.
• High comfort working with distributed & parallel workloads.
• Hands-on experience with some-many of these technologies : Apache Spark, Apache Flink,



Trino, Apache Kafka, Apache Pulsar, Apache Beam

• Advanced experience in Python (preferred) or Java (strong fundamentals).
• Strong understanding of Parquet, Apache Iceberg, and Iceberg REST catalogs (Polaris /



Gravitino).

• Experience with OLAP databases — ClickHouse / StarRocks.
• Experience with semantic layers — Cube.js or similar.
• Strong experience building pipelines with Airflow, DBT, Dagster, SQLMesh.



Foundational Strengths

• Solid understanding of data structures & algorithms — sorting, searching, memory models.
• Strong grasp of OLTP vs OLAP, indexing, query execution, and storage formats.
• Ability to debug distributed systems end-to-end (compute, storage, network, orchestration).
• Familiarity with cloud environments, containerization (Docker), and monitoring.
• Experience with large-scale data — high throughput, billions of rows, large parallel workloads.
• Awareness of cost optimization in compute & storage.



Good to Have

• Experience with emerging stream processors — Dagster, RisingWave, Arroyo.
• Kubernetes, Terraform, or cloud-native big-data stacks.



Mindset

• Strong ownership — takes systems from design → build → monitor.
• Self-driven, independent, and comfortable making technical decisions.
• High attention to reliability, data accuracy, and operational excellence.
• Naturally grows into broader technical responsibility as the platform scales.



Why 1DS is a great choice

• High-trust, no-politics culture — we value communication, ownership, and accountability
• Collaborative, ego-free team — building together is in our DNA
• Learning-first environment — mentorship, peer reviews, and exposure to real business impact
• Modern stack + autonomy — your voice shapes how we build
• VC-funded & scaling fast — 250+ strong, building from India for the world



How to apply for this opportunity?

• Step 1: Click On Apply! And Register or Login on our portal.
• Step 2: Complete the Screening Form & Upload updated Resume
• Step 3: Increase your chances to get shortlisted & meet the client for the Interview!



About Uplers:

Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement.

(Note: There are many more opportunities apart from this on the portal. Depending on the assessments you clear, you can apply for them as well).

So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!

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 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

Pulsar Primary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Spark Primary Library skill 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

Apache Spark Primary Library skill 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

Apache Flink Secondary Library skill 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

Apache Iceberg Primary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Polaris Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Gravitino Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
ClickHouse Primary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
StarRocks Primary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Airflow Primary Library skill Existing skill (matched library)
Canonical: Airflow id=265 · airflow

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Workflow Orchestration for ML Pipelines Catalog dimension db id 54

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

dbt Primary Library skill Existing skill (matched library)
Canonical: dbt id=115 · dbt

Aliases — catalog

  • dbt (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

Dagster Primary Library skill Existing skill (matched library)
Canonical: Dagster id=111 · dagster

Aliases — catalog

  • Dagster (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Data Pipeline Orchestration Catalog dimension db id 23

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Workflow Orchestration for ML Pipelines Catalog dimension db id 54

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

SQLMesh Primary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Trino Primary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
SQL Primary Library skill Existing skill (matched library)
Canonical: SQL id=101 · sql

Aliases — catalog

  • SQL (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

Python Primary Library skill 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

Java Secondary Library skill Existing skill (matched library)
Canonical: Java id=1 · java

Aliases — catalog

  • Java (CANONICAL) primary
  • JDK (VERSION)
  • JDK 10 (VERSION)
  • JDK 11 (VERSION)
  • JDK 12 (VERSION)
  • JDK 13 (VERSION)
  • JDK 14 (VERSION)
  • JDK 15 (VERSION)
  • JDK 16 (VERSION)
  • JDK 17 (VERSION)
  • JDK 18 (VERSION)
  • JDK 19 (VERSION)
  • JDK 20 (VERSION)
  • JDK 21 (VERSION)
  • JDK 5 (VERSION)
  • JDK 6 (VERSION)
  • JDK 7 (VERSION)
  • JDK 8 (VERSION)
  • JDK 9 (VERSION)
  • Java 1.0 (VERSION)
  • Java 1.1 (VERSION)
  • Java 1.2 (VERSION)
  • Java 1.3 (VERSION)
  • Java 1.4 (VERSION)
  • Java 1.5 (VERSION)
  • Java 1.6 (VERSION)
  • Java 1.7 (VERSION)
  • Java 1.8 (VERSION)
  • Java 10 (VERSION)
  • Java 11 (VERSION)
  • Java 12 (VERSION)
  • Java 13 (VERSION)
  • Java 14 (VERSION)
  • Java 15 (VERSION)
  • Java 16 (VERSION)
  • Java 17 (VERSION)
  • Java 18 (VERSION)
  • Java 19 (VERSION)
  • Java 20 (VERSION)
  • Java 21 (VERSION)
  • Java 5 (VERSION)
  • Java 6 (VERSION)
  • Java 7 (VERSION)
  • Java 8 (VERSION)
  • Java 9 (VERSION)
  • Java11 (VERSION)
  • Java17 (VERSION)
  • Java21 (VERSION)
  • Java8 (VERSION)
  • OpenJDK 11 (VERSION)
  • OpenJDK 17 (VERSION)
  • OpenJDK 21 (VERSION)
  • OpenJDK 8 (VERSION)
  • java 11 (VERSION)
  • java 17 (VERSION)
  • java 21 (VERSION)
  • java 4 (VERSION)
  • java 5 (VERSION)
  • java 6 (VERSION)
  • java 7 (VERSION)
  • java 8 (VERSION)
  • java lts (VERSION)
  • java-11 (VERSION)
  • java-17 (VERSION)
  • java-21 (VERSION)
  • java-4 (VERSION)
  • java-5 (VERSION)
  • java-6 (VERSION)
  • java-7 (VERSION)
  • java-8 (VERSION)
  • java11 (VERSION)
  • java17 (VERSION)
  • java21 (VERSION)
  • java4 (VERSION)
  • java5 (VERSION)
  • java6 (VERSION)
  • java7 (VERSION)
  • java8 (VERSION)
  • jdk 11 (VERSION)
  • jdk 17 (VERSION)
  • jdk 21 (VERSION)
  • jdk 4 (VERSION)
  • jdk 5 (VERSION)
  • jdk 6 (VERSION)
  • jdk 7 (VERSION)
  • jdk 8 (VERSION)
  • jdk11 (VERSION)
  • jdk17 (VERSION)
  • jdk21 (VERSION)
  • jdk4 (VERSION)
  • jdk5 (VERSION)
  • jdk6 (VERSION)
  • jdk7 (VERSION)
  • jdk8 (VERSION)
  • jvm21 (VERSION)

Context tags (catalog)

APIs Apache Tomcat Concurrency Design patterns Garbage collection GraalVM Gradle Hibernate JDBC JDK JPA JUnit JVM Java 8 Java EE JavaFX Kafka Lambda expressions Maven Microservices Mockito Object-oriented REST RESTful SOAP Servlets Spring Spring Boot Tomcat microservices

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
Oracle
License
other_open
Year introduced
1995
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
21

Maturity reasoning: Java is a hiring-pipeline staple with very high JD volume across enterprise backend, Android, and cloud roles; it remains widely supported by major vendors and frameworks like Spring.

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)

  • Java Language and JVM Catalog dimension db id 279

    Library dimension (catalog)

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

  • Kotlin and Java Catalog dimension db id 161

    Library dimension (catalog)

    Roles linked in library: Android Developer

  • Native Mobile Languages Catalog dimension db id 274

    Library dimension (catalog)

    Roles linked in library: Native Mobile Developer

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

Parquet Primary Library skill Existing skill (matched library)
Canonical: Parquet id=173 · parquet

Aliases — catalog

  • Parquet (CANONICAL) primary

Context tags (catalog)

Apache Spark Athena Avro ETL Gzip Hive ORC Presto PyArrow Snappy Trino columnar storage data lake partitioning schema evolution

Stored enrichment (catalog DB)

Category
Format
Sub-category
Columnar File Format
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2013
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Widely used in data engineering and analytics; frequently appears in JDs for Spark/Databricks/Big Data roles and is a standard storage format in cloud data lakes.

Skill profile (library / DB)

Skill nature
STANDARD
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
4
Sub-category id
87
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Serialization Standards & Protocols Catalog dimension db id 37

    Library dimension (catalog)

    Roles linked in library: Data Engineer

Cube.js Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Docker Secondary Library skill 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

Kubernetes Secondary Library skill 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

Terraform Secondary Library skill Existing skill (matched library)
Canonical: Terraform id=286 · terraform

Aliases — catalog

  • Terraform (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Infrastructure & Security Automation Frameworks Catalog dimension db id 249

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Infrastructure as Code Catalog dimension db id 132

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, DevOps Engineer

  • Infrastructure as Code for ML Catalog dimension db id 57

    Library dimension (catalog)

    Roles linked in library: ML Engineer

Distributed Systems Primary Library skill Existing skill (matched library)
Canonical: Distributed Systems id=1369 · distributed-systems

Aliases — catalog

  • Distributed Systems (CANONICAL)

Context tags (catalog)

CAP theorem Docker Swarm Kafka MapReduce Zookeeper consensus algorithms distributed databases eventual consistency fault tolerance gRPC load balancing message queues microservices replication sharding

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Distributed Systems
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common hiring requirement in backend/platform JDs at large tech firms; appears across AWS, Kafka, microservices, and systems roles, with strong GitHub/Stack Overflow activity and no sunset signal.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1035
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer

  • Performance and Scalability Tuning Catalog dimension db id 11

    Library dimension (catalog)

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

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

OLTP Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
OLAP Primary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Indexing Secondary Library skill Existing skill (matched library)
Canonical: indexing id=20 · indexing

Aliases — catalog

  • indexing (CANONICAL) primary

Context tags (catalog)

B-tree Elasticsearch Lucene NoSQL NoSQL indexing SQL indexing bitmap index bitmap indexes cardinality clustered index columnstore index composite index composite indexes covering index data retrieval data warehousing database optimization database performance denormalization execution plan foreign key full-text search hash indexing index fragmentation index maintenance index scan index seek index structure indexing overhead indexing strategies indexing strategy inverted index materialized view non-clustered index nonclustered index normalization partitioned index partitioning primary key query optimization query optimizer query performance search algorithms secondary index secondary indexes selectivity unique index

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Database Indexing
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Database indexing is a standard requirement in SQL/NoSQL job descriptions and core DB docs; it’s broadly expected for performance tuning rather than a niche specialty.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
2477
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

    Roles linked in library: Fullstack Developer, PHP Backend Developer

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

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

  • Search and Content Discovery Catalog dimension db id 356

    Library dimension (catalog)

    Roles linked in library: Drupal Dev, Sitecore Dev

Query Optimization Secondary Library skill Existing skill (matched library)
Canonical: query optimization id=160 · query-optimization

Aliases — catalog

  • query optimization (CANONICAL) primary
  • Query optimization (CANONICAL)

Context tags (catalog)

B-tree EXPLAIN SQL performance caching cardinality estimation cost-based optimizer covering index data retrieval database partitioning database schema database tuning denormalization execution plan index scan indexing join algorithms join order join strategies load balancing materialized view materialized views normalization partition pruning partitioning query plan query profiling query rewriting slow query log statistics subqueries table scan

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Performance Optimization Concept
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in DB/perf job descriptions and interview loops; vendors like PostgreSQL, MySQL, and SQL Server all document EXPLAIN/ANALYZE and indexing as standard tuning practices.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
679
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Performance and Cost Optimization Catalog dimension db id 33

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Performance and Scalability Tuning Catalog dimension db id 11

    Library dimension (catalog)

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

Query Planning Secondary Library skill Existing skill (matched library)
Canonical: query plans id=2232 · query-plans

Aliases — catalog

  • query plans (CANONICAL) primary
  • Query plans (CANONICAL)

Context tags (catalog)

EXPLAIN SQL Server SQL performance cardinality estimation cost estimation database indexing database statistics database tuning execution plan explain plan index usage join algorithms join strategies optimizer hints performance metrics performance tuning plan caching query execution query optimization query profiling query rewriting

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Query Plan
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Query plans are a standard database tuning concept; they appear in JDs for SQL performance work and are exposed by major vendors like PostgreSQL, MySQL, SQL Server, and Oracle via EXPLAIN/EXPLAIN ANALYZE.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
2616
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Database Usage Catalog dimension db id 371

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

  • Relational Querying and Transactions Catalog dimension db id 281

    Library dimension (catalog)

    Roles linked in library: Java Backend Developer

Window Functions Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Checkpointing Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Replay Logic Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Lineage Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Anomaly Detection Secondary Library skill Existing skill (matched library)
Canonical: Anomaly detection id=134 · anomaly-detection

Aliases — catalog

  • Anomaly detection (CANONICAL) primary

Context tags (catalog)

CUSUM EWMA Mahalanobis distance autoencoder automated alerts change point detection control charts data drift density estimation false positives feature engineering isolation forest machine learning model validation monitoring novelty detection one-class SVM outlier outlier detection predictive maintenance real-time analysis root cause analysis seasonality statistical methods thresholding time series unsupervised learning z-score

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Ml Monitoring Concept
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in ML/observability job descriptions and vendor docs (Datadog, Splunk, AWS, Azure) for fraud, monitoring, and alerting; broad market adoption across production systems.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1117
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Quality and Reconciliation Catalog dimension db id 27

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Model Monitoring and Drift Detection Catalog dimension db id 45

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

Data Observability Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
ETL Primary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Pipelines Primary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Lakehouse Primary Library skill Existing skill (matched library)
Canonical: Lakehouse id=1359 · lakehouse

Aliases — catalog

  • Lakehouse (CANONICAL)

Context tags (catalog)

Apache Spark Delta Lake ETL SQL analytics cloud storage data governance data integration data lake data modeling data pipeline data warehouse metadata management real-time processing streaming analytics

Stored enrichment (catalog DB)

Category
Architecture
Sub-category
Data Platform Architecture
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Lakehouse is increasingly listed in data-platform JDs and vendor docs (Databricks, Snowflake, Microsoft Fabric), but it is not yet as universal as core warehouse or lake skills.

Skill profile (library / DB)

Skill nature
PATTERN
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
1
Sub-category id
1026
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

Semantic Layers Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Monitoring Secondary Library skill Existing skill (matched library)
Canonical: Monitoring id=1218 · monitoring

Aliases — catalog

  • Monitoring (CANONICAL)

Context tags (catalog)

ELK Stack Grafana Prometheus SLI SLO alerting anomaly detection dashboards health checks incident response logging metrics monitoring as code observability tracing

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Observability Monitoring
Confidence
0.88
Version strategy
NOT_APPLICABLE

Maturity reasoning: Monitoring is a standard requirement in most SRE/DevOps job descriptions and is bundled into major platforms like AWS CloudWatch, Datadog, and Prometheus, indicating broad market adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
924
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Observability and Incident Triage Catalog dimension db id 155

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

Containerization Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Encryption Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Access Control Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Distributed Execution Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Parallel Workloads Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Query Execution Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Storage Formats Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Big Data Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
RisingWave Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Arroyo Secondary New / orchestrated 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
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED

Library artifacts (this run)

No artifact rows for this run.
nano JD Parser — gpt-4.1-nano click to toggle
RoleSenior Data Engineer
Company1DigitalStack.ai
Experience5-7 Years
CTC{'max': None, 'min': None, 'raw': 'Confidential (based on experience)', 'period': None, 'currency': None}
DomainIT Services & Consulting
Location India (remote)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "1DigitalStack.ai combines AI and deep",
      "last_5_words": "brands win on digital shelves."
    },
    "text": "1DigitalStack.ai combines AI and deep eCommerce data to help global brands grow faster on online marketplaces. Our platforms deliver advanced analytics, actionable intelligence, and media automation \u2014 enabling brands to optimize visibility, efficiency, and sales performance at scale. We partner with India\u2019s top consumer companies \u2014 Unilever, Marico, Coca-Cola, Tata Consumer, Dabur, and Unicharm \u2014 across 125+ marketplaces globally. Backed by leading venture investors and powered by a 220+ member team, we\u2019re in our $5\u201310M growth journey, scaling rapidly across categories and geographies to redefine how brands win on digital shelves.",
    "word_count": 84
  },
  "certifications": [],
  "company_name": "1DigitalStack.ai",
  "ctc": {
    "currency": null,
    "max": null,
    "min": null,
    "period": null,
    "raw": "Confidential (based on experience)"
  },
  "domain": {
    "primary": {
      "aliases": [
        "Tech Consulting",
        "IT Solutions"
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API 1 — extract-from-jd click to toggle
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        "is_primary": true,
        "queue_id": 5613,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Apache Iceberg",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5614,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Polaris",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5615,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Gravitino",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 5616,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ClickHouse",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 5617,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "StarRocks",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 5618,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "SQLMesh",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 5619,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Trino",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5620,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Cube.js",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5621,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "OLTP",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 5622,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "OLAP",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5623,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Query Planning",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5624,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Window Functions",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5625,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Checkpointing",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5626,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Replay Logic",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5627,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Lineage",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5629,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Observability",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 5632,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 5634,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Pipelines",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5635,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Semantic Layers",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5637,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Containerization",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5639,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Encryption",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5641,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Access Control",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5643,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Distributed Execution",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5644,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Parallel Workloads",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5645,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Query Execution",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5648,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Storage Formats",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5649,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Big Data",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5650,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "RisingWave",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 5651,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Arroyo",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
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      "alias_persisted": false,
      "existing_alias_id": 173,
      "existing_alias_text": "Kafka",
      "input_term": "Kafka",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "Kafka",
        "id": 36,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "kafka",
        "sub_category_id": 3533,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2510,
      "existing_alias_text": "spark",
      "input_term": "Spark",
      "matched_canonical": {
        "category_id": 5,
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
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        "sub_category_id": 1021,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2004,
      "existing_alias_text": "Apache Spark",
      "input_term": "Apache Spark",
      "matched_canonical": {
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        "display_name": "Apache Spark",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-spark",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 314,
      "existing_alias_text": "Apache Flink",
      "input_term": "Apache Flink",
      "matched_canonical": {
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        "display_name": "Apache Flink",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-flink",
        "sub_category_id": 94,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 526,
      "existing_alias_text": "Airflow",
      "input_term": "Airflow",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Airflow",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "airflow",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 309,
      "existing_alias_text": "dbt",
      "input_term": "dbt",
      "matched_canonical": {
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        "display_name": "dbt",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "dbt",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 305,
      "existing_alias_text": "Dagster",
      "input_term": "Dagster",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Dagster",
        "id": 111,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "dagster",
        "sub_category_id": 1161,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 271,
      "existing_alias_text": "SQL",
      "input_term": "SQL",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "SQL",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "sql",
        "sub_category_id": 97,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 67,
      "existing_alias_text": "Python",
      "input_term": "Python",
      "matched_canonical": {
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        "display_name": "Python",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "python",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1,
      "existing_alias_text": "Java",
      "input_term": "Java",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Java",
        "id": 1,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "java",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 382,
      "existing_alias_text": "Parquet",
      "input_term": "Parquet",
      "matched_canonical": {
        "category_id": 4,
        "display_name": "Parquet",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "STANDARD",
        "slug": "parquet",
        "sub_category_id": 87,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 198,
      "existing_alias_text": "Docker",
      "input_term": "Docker",
      "matched_canonical": {
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        "display_name": "Docker",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "docker",
        "sub_category_id": 63,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1267,
      "existing_alias_text": "Kubernetes",
      "input_term": "Kubernetes",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Kubernetes",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "kubernetes",
        "sub_category_id": 557,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 547,
      "existing_alias_text": "Terraform",
      "input_term": "Terraform",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Terraform",
        "id": 286,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "terraform",
        "sub_category_id": 191,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2028,
      "existing_alias_text": "Distributed Systems",
      "input_term": "Distributed Systems",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "Distributed Systems",
        "id": 1369,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
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        "sub_category_id": 1035,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 148,
      "existing_alias_text": "indexing",
      "input_term": "Indexing",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "indexing",
        "id": 20,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "indexing",
        "sub_category_id": 2477,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 369,
      "existing_alias_text": "Query optimization",
      "input_term": "Query Optimization",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "query optimization",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "query-optimization",
        "sub_category_id": 679,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 3490,
      "existing_alias_text": "Query plans",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
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        "typical_lifespan": "EVERGREEN",
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      },
      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 338,
      "existing_alias_text": "Anomaly detection",
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        "category_id": 2,
        "display_name": "Anomaly detection",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2018,
      "existing_alias_text": "Lakehouse",
      "input_term": "Lakehouse",
      "matched_canonical": {
        "category_id": 1,
        "display_name": "Lakehouse",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "lakehouse",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1854,
      "existing_alias_text": "Monitoring",
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      "matched_canonical": {
        "category_id": 2,
        "display_name": "Monitoring",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "monitoring",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
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  ],
  "candidate_roles": [
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      "rationale": null,
      "role_archetype": "Engineering",
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      "display_name": "ML Engineer",
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      "slug": "ml-ops-engineer",
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      "display_name": "Pega Developer",
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      "rationale": null,
      "role_archetype": null,
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        "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
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        "slug": "programming-languages-for-data-work",
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    {
      "dimension": {
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        "display_name": "Cloud Security Scripting \u0026 DSL Languages",
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        "source": "db"
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        "source": "db"
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          "rationale": null,
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    {
      "dimension": {
        "difficulty_hint": "well_known",
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        "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.",
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      "input_skill": "Python",
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        "difficulty_hint": "well_known",
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        "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.",
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    },
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      "dimension": {
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        "source": "db"
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          "rationale": null,
          "role_archetype": "Engineering",
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        "rationale": "Primary implementation languages for Android app features, platform integration, and client-side business logic. Android engineers use these languages to build screens, state flows, service adapters, and device-aware behavior.",
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        "display_name": "Native Mobile Languages",
        "id": 274,
        "rationale": "Primary implementation languages used to build platform-specific app features, UI logic, and device integrations. This is the core coding surface for native mobile work on one platform.",
        "slug": "native-mobile-languages",
        "source": "db"
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      "input_skill": "Java",
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          "display_name": "Native Mobile Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
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      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Pega Programming Languages \u0026 DSLs",
        "id": 267,
        "rationale": "Programming languages and domain-specific languages used in Pega development.",
        "slug": "pega-programming-languages-dsls",
        "source": "db"
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      "input_skill": "Java",
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          "display_name": "Pega Developer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "pega-developer",
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          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
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          "display_name": "Fullstack Developer",
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          "slug": "full-stack-engineer",
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    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for Data Work",
        "id": 21,
        "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
        "slug": "programming-languages-for-data-work",
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      "input_skill": "Java",
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      "roles_from_db": [
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          "display_name": "Data Engineer",
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          "slug": "data-engineer",
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      "dimension": {
        "difficulty_hint": "well_known",
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        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "risingwave",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Arroyo",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Other",
          "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": "arroyo",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Pulsar",
    "Apache Iceberg",
    "Polaris",
    "Gravitino",
    "ClickHouse",
    "StarRocks",
    "SQLMesh",
    "Trino",
    "Cube.js",
    "OLTP",
    "OLAP",
    "Window Functions",
    "Checkpointing",
    "Replay Logic",
    "Data Lineage",
    "Data Observability",
    "ETL",
    "Data Pipelines",
    "Semantic Layers",
    "Containerization",
    "Encryption",
    "Access Control",
    "Distributed Execution",
    "Parallel Workloads",
    "Query Execution",
    "Storage Formats",
    "Big Data",
    "RisingWave",
    "Arroyo"
  ]
}
API 3 — final-role-output
{}

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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