Pipeline run
8d04f227-471b-455e-908a-0c204280812c
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Data Engineer
CASE Aslug: data-engineer · id: 2 · source: db
Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top data-engineer 0.45 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
A bit about us: PulsePoint is a leading technology company that uses real-world data in real-time to optimize campaign performance and revolutionize health decision-making. Leveraging proprietary datasets and methodology, PulsePoint targets healthcare professionals and patients with an unprecedented level of accuracy—delivering unparalleled results to the clients we serve. The company is now a part of Internet Brands, a KKR portfolio company and owner of WebMD Health Corp. Data Engineer PulsePoint Data Engineering team plays a key role in our technology company that’s experiencing exponential growth. Our data pipeline processes over 80 billion impressions a day (> 20TB of data, 220 TB uncompressed). This data is used to generate reports, update budgets, and drive our optimization engines. We do all this while running against extremely tight SLAs and provide stats and reports as close to real-time as possible. The most exciting part about working at PulsePoint is the enormous potential for personal and professional growth. We are always seeking new and better tools to help us meet challenges such as adopting proven open-source technologies to make our data infrastructure more nimble, scalable and robust. Some of the cutting-edge technologies we have recently implemented are Kafka, Spark Streaming, Presto, Airflow, and Kubernetes. What you'll be doing: • Design, build, and maintain reliable and scalable enterprise-level distributed transactional data processing systems for scaling the existing business and supporting new business initiatives • Optimize jobs to utilize Kafka, Hadoop, Presto, Spark, and Kubernetes resources in the most efficient way • Monitor and provide transparency into data quality across systems (accuracy, consistency, completeness, etc) • Increase accessibility and effectiveness of data (work with analysts, data scientists, and developers to build/deploy tools and datasets that fit their use cases) • Collaborate within a small team with diverse technology backgrounds • Provide mentorship and guidance to junior team members Team Responsibilities: • Ingest, validate and process internal & third party data • Create, maintain and monitor data flows in Spark, Hive, SQL and Presto for consistency, accuracy and lag time • Maintain and enhance framework for jobs(primarily aggregate jobs in Spark and Hive) • Create different consumers for data in Kafka using Spark Streaming for near time aggregation • Tool evaluation/selection/implementation • Backups/Retention/High Availability/Capacity Planning • Review/Approval - DDL for database, Hive Framework jobs and Spark Streaming to make sure they meet our standards Technologies We Use: • Airflow - for job scheduling • Docker - Packaged container image with all dependencies • Graphite/Beacon - for monitoring data flows • Hive - SQL data warehouse layer for data in HDFS • Kafka- distributed commit log storage • Kubernetes - Distributed cluster resource manager • Presto - fast parallel data warehouse and data federation layer • Spark Streaming - Near time aggregation • SQL Server - Reliable OLTP RDBMS • GCP BQ Requirements: • 5+ years of data engineering experience • Fluency in Python, experience in Scala/Java is a huge plus (Polyglot programmer preferred!) • Hive experience • Proficiency in Linux • Strong understanding of RDBMS, SQL; • Passion for engineering and computer science around data • Willing and able to work East Coast U.S. hours (9am-6pm EST) • Willingness to participate in 24x7 on-call rotation • Knowledge and exposure to distributed production systems i.e Hadoop is a huge plus • Knowledge and exposure to Cloud migration is a plus Selection Process: 1) Initial Screen (30 mins) 2) Hiring Manager Interview (45 mins) 3) Tech Challenge 4) Team Interview (60 mins + 3 x 45 mins) + SVP of Engineering (15 mins) 5) WebMD Sr. Director, DBA (30 mins) WebMD and its affiliates is an Equal Opportunity/Affirmative Action employer and does not discriminate on the basis of race, ancestry, color, religion, sex, gender, age, marital status, sexual orientation, gender identity, national origin, medical condition, disability, veterans status, or any other basis protected by law.
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
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)
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 saved |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Scala (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- EPFL
- License
- apache_2
- Year introduced
- 2004
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Scala still appears in many backend/data engineering JDs, especially with Spark and Akka, and remains supported by major JVM ecosystems; it’s not a sunset technology.
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)
-
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
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
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)
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
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Java Language and JVM
java-language-and-jvm
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Kotlin and Java
kotlin-and-java
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Native Mobile Languages
native-mobile-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | 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 for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Apache Spark (CANONICAL)
- apache spark 3 (VERSION)
- spark (VERSION)
- spark 3 (VERSION)
- spark 3.x (VERSION)
- spark3 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Distributed Data Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.94
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3.x
Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 1021
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ETL and ELT Tooling Catalog dimension db id 24
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- DStreams (VERSION)
- Spark 2.x (VERSION)
- Spark 3.x (VERSION)
- Spark Streaming (VERSION)
- Spark Structured Streaming (VERSION)
- Structured Streaming (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Stream Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2013
- Confidence
- 0.90
- Version strategy
- SEPARATE_ENTITY
- Version tag
- Structured Streaming (Spark 2.0+)
Maturity reasoning: JD volume is far lower than Structured Streaming; most Spark streaming roles now specify Structured Streaming or Kafka/Flink, and Spark docs position Spark Streaming as the older API.
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 saved |
Aliases — catalog
- Hive (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Local Key Value Store
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Hive appears in Flutter/mobile JDs and package docs, but JD volume is far below SQLite/Realm and it’s mainly used for local key-value storage in Flutter apps.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 2242
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Local Persistence and Offline Behavior Catalog dimension db id 85
Library dimension (catalog)
Roles linked in library: Android Developer, Flutter Developer, Hybrid Mobile Developer, Native Mobile Developer, React Native Developer, iOS Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- SQL (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Query Language
- Vendor
- ANSI
- License
- unknown
- Year introduced
- 1974
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: SQL appears in a large share of data, backend, and analytics job descriptions and remains the default query language for PostgreSQL, MySQL, and cloud warehouses like Snowflake/BigQuery.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 97
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Pega Programming Languages & DSLs Catalog dimension db id 267
Library dimension (catalog)
Roles linked in library: Pega Developer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Kafka (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Event Stream Store
- Vendor
- Confluent
- License
- apache_2
- Year introduced
- 2011
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Kafka appears in many production JDs for event streaming and data pipelines, and remains a standard platform in cloud/vendor offerings (e.g., Confluent, AWS MSK), indicating broad hiring demand.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 3533
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Asynchronous Messaging and Event Streaming Catalog dimension db id 297
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Go Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Scala Backend Developer
-
Messaging and Background Jobs Catalog dimension db id 291
Library dimension (catalog)
Roles linked in library: PHP Backend Developer, Python Backend Developer, Ruby Backend Developer
-
Messaging and Event Streaming Catalog dimension db id 8
Library dimension (catalog)
Roles linked in library: Backend Developer, Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Messaging and Background Jobs
messaging-and-background-jobs
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Messaging and Event Streaming
messaging-and-event-streaming
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Hadoop (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Data Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2006
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Job postings still mention Hadoop for legacy big-data stacks, but JD volume has fallen as Spark and cloud warehouses replaced MapReduce-era clusters.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 91
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ETL and ELT Tooling Catalog dimension db id 24
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
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)
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) |
Aliases — catalog
- Airflow (CANONICAL) primary
- airflow 2 (VERSION)
- airflow-2 (VERSION)
- airflow2 (VERSION)
- airflow2.x (VERSION)
- apache airflow 2 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Workflow Orchestration Tool
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2014
- Confidence
- 0.95
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 2.x
Maturity reasoning: Apache Airflow appears in many data engineering job postings and is a common orchestration choice in production stacks; its GitHub activity and ecosystem remain strong, with no vendor sunset or clear replacement dominating JDs.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 130
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Workflow Orchestration for ML Pipelines Catalog dimension db id 54
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Docker (CANONICAL) primary
Context tags (catalog)
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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Monitoring Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Operating Systems
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Aliases — catalog
- SQL Server (CANONICAL) primary
- SQL Server 2000 (VERSION)
- SQL Server 2005 (VERSION)
- SQL Server 2008 (VERSION)
- SQL Server 2012 (VERSION)
- SQL Server 2014 (VERSION)
- SQL Server 2016 (VERSION)
- SQL Server 2017 (VERSION)
- SQL Server 2019 (VERSION)
- SQL Server 2022 (VERSION)
- SQL Server 6.5 (VERSION)
- SQL Server 7.0 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Relational Database
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 1989
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: SQL Server appears in many enterprise job descriptions and remains a major Microsoft-supported RDBMS with active Azure SQL/SQL Server demand; it is a common hiring-pipeline staple, not a sunset technology.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 29
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Relational Database Design Catalog dimension db id 4
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Relational Database Design
relational-database-design
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- BigQuery (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Data Warehouse Service
- Vendor
- License
- proprietary
- Year introduced
- 2011
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: BigQuery appears frequently in data/analytics job descriptions and is a core Google Cloud warehouse offering, with broad enterprise adoption and strong ecosystem support.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 118
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Data Warehouses Catalog dimension db id 22
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Data Warehouses
cloud-data-warehouses
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- RDBMS (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Relational Database Management System
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: RDBMS is a core requirement in many job descriptions across backend, data, and DBA roles; PostgreSQL, MySQL, and SQL Server remain standard enterprise stacks.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 1023
- 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 |
|---|---|---|---|---|---|---|
| Python | in_db |
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Python | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Scala | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Scala | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Java Language and JVM
java-language-and-jvm
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Kotlin and Java
kotlin-and-java
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Native Mobile Languages
native-mobile-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Spark | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Spark Streaming | in_db |
Stream Processing Systems
stream-processing-systems
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Hive | in_db |
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Kafka | in_db |
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Kafka | in_db |
Messaging and Background Jobs
messaging-and-background-jobs
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Kafka | in_db |
Messaging and Event Streaming
messaging-and-event-streaming
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Hadoop | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| 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) | |
| Airflow | in_db |
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
|
✓ | — | 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) | |
| SQL Server | in_db |
Relational Database Design
relational-database-design
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Google BigQuery | new |
Cloud Data Warehouses
cloud-data-warehouses
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| RDBMS | 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 | Presto | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Graphite | type=Monitoring Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Linux | type=Operating Systems subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | HDFS | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Google BigQuery ↔ Cloud Data Warehouses | |
| role_dimension_link_proposed | Data Engineer ↔ Cloud Data Warehouses |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "PulsePoint is a leading",
"last_5_words": "owner of WebMD Health Corp."
},
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"Data Engineer",
"Data Developer",
"Data Pipeline Engineer"
],
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{
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"last_5_words": "to junior team members"
},
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},
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"source_marker": {
"first_5_words": "Requirements:",
"last_5_words": "is a plus"
},
"text": "\u2022 5+ years of data engineering experience\n\u2022 Fluency in Python, experience in Scala/Java is a huge plus (Polyglot programmer preferred!)\n\u2022 Hive experience\n\u2022 Proficiency in Linux\n\u2022 Strong understanding of RDBMS, SQL;\n\u2022 Passion for engineering and computer science around data\n\u2022 Willing and able to work East Coast U.S. hours (9am-6pm EST)\n\u2022 Willingness to participate in 24x7 on-call rotation\n\u2022 Knowledge and exposure to distributed production systems i.e Hadoop is a huge plus\n\u2022 Knowledge and exposure to Cloud migration is a plus",
"word_count": 104
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": false,
"skill_name": "Scala"
},
{
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{
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{
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{
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{
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{
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},
{
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{
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{
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{
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{
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{
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{
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{
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],
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],
"role_archetype": "Data",
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},
"text": "\u2022 Ingest, validate and process internal \u0026 third party data\n\u2022 Create, maintain and monitor data flows in Spark, Hive, SQL and Presto for consistency, accuracy and lag time\n\u2022 Maintain and enhance framework for jobs(primarily aggregate jobs in Spark and Hive)\n\u2022 Create different consumers for data in Kafka using Spark Streaming for near time aggregation\n\u2022 Tool evaluation/selection/implementation\n\u2022 Backups/Retention/High Availability/Capacity Planning\n\u2022 Review/Approval - DDL for database, Hive Framework jobs and Spark Streaming to make sure they meet our standards",
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},
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"word_count": 66
},
{
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"heading": "Requirements",
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"source_marker": {
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},
"text": "\u2022 5+ years of data engineering experience\n\u2022 Fluency in Python, experience in Scala/Java is a huge plus (Polyglot programmer preferred!)\n\u2022 Hive experience\n\u2022 Proficiency in Linux\n\u2022 Strong understanding of RDBMS, SQL;\n\u2022 Passion for engineering and computer science around data\n\u2022 Willing and able to work East Coast U.S. hours (9am-6pm EST)\n\u2022 Willingness to participate in 24x7 on-call rotation\n\u2022 Knowledge and exposure to distributed production systems i.e Hadoop is a huge plus\n\u2022 Knowledge and exposure to Cloud migration is a plus",
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}
],
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},
"rejected": false,
"rejection_reason": null,
"run_id": "8d04f227-471b-455e-908a-0c204280812c",
"stage3_signals": {
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{
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"matched_skills": null,
"role_id": 2,
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}
],
"kra_match_roles": [
{
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{
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"similarity": 0.6829
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{
"kra_text": "Implements data quality validation rules, reconciliation checks, and anomaly detection to ensure data completeness, accuracy, and consistency.",
"sentence": "Monitor and provide transparency into data quality across systems (accuracy, consistency, completeness, etc)",
"similarity": 0.6325
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
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}
],
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"score": 0.6452,
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},
{
"display_name": "Cloud Architect",
"kra_matches": [
{
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"sentence": "Backups/Retention/High Availability/Capacity Planning",
"similarity": 0.5864
},
{
"kra_text": "Defines cloud adoption roadmaps, lift-and-shift vs. refactor migration strategies, and landing zone architectures for workloads moving to AWS, Azure, or GCP.",
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"similarity": 0.5126
},
{
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"sentence": "Design, build, and maintain reliable and scalable enterprise-level distributed transactional data processing systems for scaling the existing business and supporting new business initiatives",
"similarity": 0.5098
}
],
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{
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],
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},
{
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{
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"case": "A",
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"new_role_display_name": null,
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"centroid_updated": true,
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"new_kra_attached": null,
"new_skills_attached": [
{
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}
API 2 — extract-details
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"existing_alias_text": "Airflow",
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{
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},
{
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{
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},
{
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},
{
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{
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{
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{
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},
{
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"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.",
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},
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{
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}
]
},
{
"dimension": {
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},
{
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{
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},
{
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},
{
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"role_archetype": null,
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]
},
{
"dimension": {
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"input_skill": "Hive",
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{
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"rationale": null,
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{
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"rationale": null,
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{
"display_name": "Hybrid Mobile Developer",
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"rationale": null,
"role_archetype": null,
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{
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"rationale": null,
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{
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"rationale": null,
"role_archetype": "Engineering",
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},
{
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"rationale": null,
"role_archetype": null,
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},
{
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"source": "db"
},
"input_skill": "SQL",
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"roles_from_db": [
{
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"rationale": null,
"role_archetype": null,
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},
{
"dimension": {
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"role_archetype": null,
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]
},
{
"dimension": {
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{
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"role_archetype": "Engineering",
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{
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"rationale": null,
"role_archetype": "Engineering",
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{
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"rationale": null,
"role_archetype": "Engineering",
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{
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},
{
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"roles_from_db": [
{
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{
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"rationale": null,
"role_archetype": "Engineering",
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{
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"rationale": null,
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},
{
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{
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},
{
"dimension": {
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},
{
"dimension": {
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{
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{
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{
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},
{
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},
{
"dimension": {
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},
{
"dimension": {
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{
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{
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{
"display_name": "PHP Backend Developer",
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},
{
"dimension": {
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{
"display_name": "Kotlin Backend Developer",
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"display_name": "Node.js Backend Developer",
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{
"display_name": "Python Backend Developer",
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"role_archetype": "Engineering",
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"source": "db"
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{
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{
"display_name": "Scala Backend Developer",
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"rationale": null,
"role_archetype": "Engineering",
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]
},
{
"dimension": {
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"rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
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},
"input_skill": "Google BigQuery",
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"roles_from_db": [
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"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Kafka",
"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": "Hadoop",
"alias_type": "CANONICAL",
"id": 2010,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Hadoop",
"id": 1351,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "hadoop",
"sub_category_id": 91,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"input_skill": "Hadoop",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Hadoop",
"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": "Kubernetes",
"alias_type": "CANONICAL",
"id": 1267,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.0+",
"alias_type": "VERSION",
"id": 1271,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.x",
"alias_type": "VERSION",
"id": 1270,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes v1",
"alias_type": "VERSION",
"id": 1269,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "k8s",
"alias_type": "VERSION",
"id": 1268,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "kubernetes 1.x",
"alias_type": "VERSION",
"id": 1400,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "kubernetes latest",
"alias_type": "VERSION",
"id": 1401,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
"display_name": "Kubernetes",
"id": 726,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "kubernetes",
"sub_category_id": 557,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Container Orchestration Platforms",
"id": 134,
"rationale": "Platforms that schedule and manage containerized workloads across clusters and environments. Cloud Architects need these to define workload placement standards, cluster boundaries, and platform capabilities.",
"slug": "container-orchestration-platforms",
"source": "db"
},
"input_skill": "Kubernetes",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Kubernetes for ML Workloads",
"id": 47,
"rationale": "Kubernetes-native components used to schedule, accelerate, and isolate ML training and serving workloads. This includes GPU enablement and ML-specific controllers rather than generic cluster administration.",
"slug": "kubernetes-for-ml-workloads",
"source": "db"
},
"input_skill": "Kubernetes",
"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"
}
]
}
],
"input_skill": "Kubernetes",
"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": "Airflow",
"alias_type": "CANONICAL",
"id": 526,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "airflow 2",
"alias_type": "VERSION",
"id": 2477,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "airflow-2",
"alias_type": "VERSION",
"id": 2478,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "airflow2",
"alias_type": "VERSION",
"id": 2476,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "airflow2.x",
"alias_type": "VERSION",
"id": 2479,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "apache airflow 2",
"alias_type": "VERSION",
"id": 2480,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Airflow",
"id": 265,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "airflow",
"sub_category_id": 130,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Workflow Orchestration for ML Pipelines",
"id": 54,
"rationale": "Workflow engines used to coordinate training, evaluation, deployment, and retraining jobs. This cluster covers dependencies, retries, scheduling, and pipeline composition for ML lifecycle automation.",
"slug": "workflow-orchestration-for-ml-pipelines",
"source": "db"
},
"input_skill": "Airflow",
"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"
}
]
}
],
"input_skill": "Airflow",
"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": "Docker",
"alias_type": "CANONICAL",
"id": 198,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Docker",
"id": 61,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "docker",
"sub_category_id": 63,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Containerization and Image Builds",
"id": 152,
"rationale": "Container image creation, tagging, hardening, and registry workflows used to package services for deployment. This is coherent because DevOps often owns the build-to-image path that feeds runtime environments.",
"slug": "containerization-and-image-builds",
"source": "db"
},
"input_skill": "Docker",
"llm_role": null,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Deployment and Cloud Platforms",
"id": 418,
"rationale": "Platform-as-a-Service and container environments for deploying Ruby applications.",
"slug": "deployment-and-cloud-platforms",
"source": "db"
},
"input_skill": "Docker",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Deployment and Runtime Configuration",
"id": 13,
"rationale": "Configuration and release artifacts that control how backend services run in environments. Includes environment variables, manifests, feature flags, and release-safe configuration management.",
"slug": "deployment-and-runtime-configuration",
"source": "db"
},
"input_skill": "Docker",
"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": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "Docker",
"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": "Graphite",
"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": "graphite",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Linux",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Operating Systems",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "linux",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "SQL Server",
"alias_type": "CANONICAL",
"id": 135,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2000",
"alias_type": "VERSION",
"id": 138,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2005",
"alias_type": "VERSION",
"id": 139,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2008",
"alias_type": "VERSION",
"id": 140,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2012",
"alias_type": "VERSION",
"id": 141,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2014",
"alias_type": "VERSION",
"id": 142,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2016",
"alias_type": "VERSION",
"id": 143,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2017",
"alias_type": "VERSION",
"id": 144,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2019",
"alias_type": "VERSION",
"id": 145,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2022",
"alias_type": "VERSION",
"id": 146,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 6.5",
"alias_type": "VERSION",
"id": 136,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 7.0",
"alias_type": "VERSION",
"id": 137,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 3,
"display_name": "SQL Server",
"id": 18,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "sql-server",
"sub_category_id": 29,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Relational Database Design",
"id": 4,
"rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
"slug": "relational-database-design",
"source": "db"
},
"input_skill": "SQL Server",
"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": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "SQL Server",
"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": "BigQuery",
"alias_type": "CANONICAL",
"id": 300,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 11,
"display_name": "BigQuery",
"id": 106,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
"slug": "bigquery",
"sub_category_id": 118,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Data Warehouses",
"id": 22,
"rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
"slug": "cloud-data-warehouses",
"source": "db"
},
"input_skill": "Google BigQuery",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Google BigQuery",
"matched_via": "embedding_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": "HDFS",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering 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": "hdfs",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "RDBMS",
"alias_type": "CANONICAL",
"id": 2014,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 3,
"display_name": "RDBMS",
"id": 1355,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "rdbms",
"sub_category_id": 1023,
"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": "RDBMS",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "RDBMS",
"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": [
"Presto",
"Graphite",
"Linux",
"HDFS"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.45 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "Scala",
"tag": "in_db"
},
{
"skill": "Java",
"tag": "in_db"
},
{
"skill": "Spark",
"tag": "in_db"
},
{
"skill": "Spark Streaming",
"tag": "in_db"
},
{
"skill": "Hive",
"tag": "in_db"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "Presto",
"tag": "new"
},
{
"skill": "Kafka",
"tag": "in_db"
},
{
"skill": "Hadoop",
"tag": "in_db"
},
{
"skill": "Kubernetes",
"tag": "in_db"
},
{
"skill": "Airflow",
"tag": "in_db"
},
{
"skill": "Docker",
"tag": "in_db"
},
{
"skill": "Graphite",
"tag": "new"
},
{
"skill": "Linux",
"tag": "new"
},
{
"skill": "SQL Server",
"tag": "in_db"
},
{
"skill": "Google BigQuery",
"tag": "in_db"
},
{
"skill": "HDFS",
"tag": "new"
},
{
"skill": "RDBMS",
"tag": "in_db"
}
],
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"slug": "scala-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 36,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Messaging and Background Jobs",
"id": 291,
"rationale": "Asynchronous processing patterns and worker systems used to decouple backend work from request handling. This is a coherent cluster because the role supports background jobs, retries, and deferred processing.",
"slug": "messaging-and-background-jobs",
"source": "db"
},
"dimension_id": 291,
"input_skill": "Kafka",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 36,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Messaging and Event Streaming",
"id": 8,
"rationale": "Transport-layer systems used to move events and decouple producers from consumers. Data engineers use these systems to ingest, buffer, and distribute event data before downstream processing.",
"slug": "messaging-and-event-streaming",
"source": "db"
},
"dimension_id": 8,
"input_skill": "Kafka",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 36,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"dimension_id": 24,
"input_skill": "Hadoop",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1351,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Container Orchestration Platforms",
"id": 134,
"rationale": "Platforms that schedule and manage containerized workloads across clusters and environments. Cloud Architects need these to define workload placement standards, cluster boundaries, and platform capabilities.",
"slug": "container-orchestration-platforms",
"source": "db"
},
"dimension_id": 134,
"input_skill": "Kubernetes",
"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"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 726,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Kubernetes for ML Workloads",
"id": 47,
"rationale": "Kubernetes-native components used to schedule, accelerate, and isolate ML training and serving workloads. This includes GPU enablement and ML-specific controllers rather than generic cluster administration.",
"slug": "kubernetes-for-ml-workloads",
"source": "db"
},
"dimension_id": 47,
"input_skill": "Kubernetes",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 726,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Workflow Orchestration for ML Pipelines",
"id": 54,
"rationale": "Workflow engines used to coordinate training, evaluation, deployment, and retraining jobs. This cluster covers dependencies, retries, scheduling, and pipeline composition for ML lifecycle automation.",
"slug": "workflow-orchestration-for-ml-pipelines",
"source": "db"
},
"dimension_id": 54,
"input_skill": "Airflow",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 265,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Containerization and Image Builds",
"id": 152,
"rationale": "Container image creation, tagging, hardening, and registry workflows used to package services for deployment. This is coherent because DevOps often owns the build-to-image path that feeds runtime environments.",
"slug": "containerization-and-image-builds",
"source": "db"
},
"dimension_id": 152,
"input_skill": "Docker",
"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": 61,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Deployment and Cloud Platforms",
"id": 418,
"rationale": "Platform-as-a-Service and container environments for deploying Ruby applications.",
"slug": "deployment-and-cloud-platforms",
"source": "db"
},
"dimension_id": 418,
"input_skill": "Docker",
"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": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 61,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Deployment and Runtime Configuration",
"id": 13,
"rationale": "Configuration and release artifacts that control how backend services run in environments. Includes environment variables, manifests, feature flags, and release-safe configuration management.",
"slug": "deployment-and-runtime-configuration",
"source": "db"
},
"dimension_id": 13,
"input_skill": "Docker",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-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": 61,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Relational Database Design",
"id": 4,
"rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
"slug": "relational-database-design",
"source": "db"
},
"dimension_id": 4,
"input_skill": "SQL Server",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 18,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Data Warehouses",
"id": 22,
"rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
"slug": "cloud-data-warehouses",
"source": "db"
},
"dimension_id": 22,
"input_skill": "Google BigQuery",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"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": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"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": "RDBMS",
"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": 1355,
"skill_tag": "in_db",
"skipped_reason": null
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 1
},
"planner_output": null,
"run_id": "8d04f227-471b-455e-908a-0c204280812c"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.