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Pipeline run

dab7bda3-791a-45d7-9c0c-8b6285c6df01

Pipeline LLM cost (USD)
API 1: $0.0032 API 2: $0.0004 API 3: $0.0000 Total: $0.0036

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 pipeline development
Build and maintain ETL/ELT pipelines in Matillion and Snowflake, pulling data from APIs, S3, and databases, writing/optimizing SQL, and monitoring jobs to keep data accurate and reliable.
"Assist in building and maintaining ETL/ELT data pipelines using Matillion or similar tools"
Tech stack maturity
Mainstream Modern
The stack centers on widely adopted cloud data platform and integration tools like Snowflake, S3, Matillion, APIs, and SQL, which are characteristic of mainstream modern data engineering.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.00 / 5
· Title match
· Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3):
Evidence — skills matched in JD (9)
ETL ELT Matillion Snowflake SQL APIs Amazon S3 Databases Orchestration
Skill cluster (5 dimension groups, role-scoped)
Cloud Data Warehouses
Snowflake
Cloud Storage and File Formats
Amazon S3
ETL and ELT Tooling
Matillion
Programming Languages for Data Work
SQL
Cross-cutting / unaligned
ETL ELT APIs Databases Orchestration
Show KRA description ↓
Assist in building and maintaining ETL/ELT data pipelines using Matillion or similar tools Work with Snowflake to store, process, and analyze data Write, optimize, and maintain SQL queries for large datasets Perform data extraction, transformation, and loading from multiple sources (APIs, S3 files, databases) Monitor and troubleshoot data workflows and pipelines Support scheduling and automation of jobs using orchestration tools Ensure data quality, consistency, and reliability Collaborate with team members and stakeholders to understand data requirements Maintain proper documentation for data processes and workflows Work in a fast-paced, collaborative environment while demonstrating ownership, analytical thinking, and problem-solving skills Continuously learn and adapt to new technologies, tools, and data engineering practices

Signals

Skill data-engineer
0.44
Alias data-engineer
1.00
KRA data-engineer
0.68

Post-classification

Centroidupdated · n=22
Alias collision log
New-role queue
New skills captured4
New KRA captured

Captured for admin review

ETL primary Data Engineer pending
ELT primary Data Engineer pending
Databases primary Data Engineer pending
Orchestration primary Data Engineer pending
Status: completed Created: 2026-05-21T14:28:37.248705Z Updated: 2026-05-21T14:28:52.439217Z API 3 duration: 4764 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Data Engineer

CASE A

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

The primary skills predominantly align with the responsibilities of a Data Engineer.

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

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

Job description

About the job
Description

Position at Spiceworks

Associate Data Engineer

____________________________________________________________________________

The Opportunity:

We are looking for enthusiastic and motivated fresh graduates to join our Data Engineering team. This role is ideal for candidates who are passionate about working with data and are eager to build a career in data engineering.

The selected candidate will work with modern data platforms and tools, gaining hands-on experience in building, maintaining, and optimizing data pipelines using technologies such as Snowflake, Matillion, AWS, and Kubernetes.

Key Responsibilities:

Assist in building and maintaining ETL/ELT data pipelines using Matillion or similar tools 
Work with Snowflake to store, process, and analyze data 
Write, optimize, and maintain SQL queries for large datasets 
Perform data extraction, transformation, and loading from multiple sources (APIs, S3 files, databases) 
Monitor and troubleshoot data workflows and pipelines
Support scheduling and automation of jobs using orchestration tools
Ensure data quality, consistency, and reliability
Collaborate with team members and stakeholders to understand data requirements
Maintain proper documentation for data processes and workflows
Work in a fast-paced, collaborative environment while demonstrating ownership, analytical thinking, and problem-solving skills
Continuously learn and adapt to new technologies, tools, and data engineering practices

Job Qualifications:

Bachelor’s degree in Computer Science, Information Technology, or a related field
0–1 years of experience, including internships, certifications, academic projects, or hands-on exposure in Data Engineering
Strong understanding of SQL, including joins, aggregations, and query optimization fundamentals
Basic knowledge of Python or any scripting language
Understanding of ETL/ELT and data warehousing concepts
Familiarity with Linux/Unix commands and environments
Basic understanding of cloud platforms, preferably AWS services such as S3, EC2, and Lambda
Exposure to Snowflake and Matillion or similar ETL/data integration tools
Knowledge of scheduling and orchestration tools such as Airflow or cron
Understanding of APIs and data formats such as JSON and CSV
Familiarity with version control tools such as Git
Strong analytical mindset, attention to detail, communication skills, and teamwork capabilities
Ability to quickly learn new technologies and work effectively in a dynamic environment

About Ziff Davis

Ziff Davis (NASDAQ: ZD) is a vertically focused digital media and internet company whose portfolio includes leading brands in technology, shopping, gaming and entertainment, connectivity, health, cybersecurity, and martech. Today, Ziff Davis is focused on seven key verticals – Technology, Connectivity, Shopping, Entertainment, Health & Wellness, Cybersecurity and Marketing Technology. Its brands include IGN, Mashable, RetailMeNot, PCMag, Humble Bundle, Spiceworks, Ookla (Speedtest), RootMetrics, Everyday Health, BabyCenter, Moz, iContact and Vipre Security.

Our Benefits

Spice Works Ziff Davis (SWZD) offers competitive salaries in addition to robust, health and wellness-focused benefits. We are committed to work-life balance with paid time off, paid holidays and extended leave of absence, when you need it.

At Ziff Davis, we remain dedicated to creating an environment where everyone feels valued, respected, and empowered to succeed. We offer Employee Resource Groups, company-sponsored events, and regular opportunities for professional growth through educational support, mentorship programs, and career development resources. Our employees are recognized and celebrated through employee engagement programs and recognition awards.

If you're seeking a dynamic and collaborative work environment where you can see the direct impact of your performance and thrive both personally and professionally, then SWZD is the place 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.

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
ELT Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Matillion Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Matillion id=118 · matillion

Aliases — catalog

  • Matillion (CANONICAL) primary

Context tags (catalog)

API connectors Amazon Redshift Azure Synapse ELT ETL Google BigQuery JDBC S3 SQL Snowflake data warehouse dbt incremental loads orchestration staging tables

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Integration Platform
Vendor
Matillion Ltd.
License
proprietary
Year introduced
2011
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Matillion appears in cloud data-integration JDs, especially for Snowflake/Databricks stacks, but volume is far below ETL staples like Informatica/dbt, indicating growing but not universal adoption.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
114
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
Snowflake Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Snowflake id=105 · snowflake

Aliases — catalog

  • Snowflake (CANONICAL) primary

Context tags (catalog)

ELT ETL SQL Snowpark Snowpipe Streams Tasks Time Travel VARIANT data sharing data warehouse dbt semi-structured data virtual warehouse zero-copy cloning

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Cloud Platform
Vendor
Snowflake Inc.
License
proprietary
Year introduced
2012
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Snowflake appears frequently in data/analytics job postings and is a standard cloud data warehouse platform alongside BigQuery and Redshift.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
113
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
Existing dimension (library) · Role↔dimension saved
SQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL id=101 · sql

Aliases — catalog

  • SQL (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • 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
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
APIs Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: APIs id=1192 · apis

Aliases — catalog

  • APIs (CANONICAL)

Context tags (catalog)

API Gateway Endpoint GraphQL JSON JWT Microservices OAuth Postman REST Rate Limiting SOAP Swagger Throttling Webhooks XML

Stored enrichment (catalog DB)

Category
Protocol
Sub-category
Application Programming Interfaces
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: APIs are a hiring-pipeline staple across backend, mobile, and platform JDs; REST/GraphQL/API design appears in large volumes of job postings and vendor docs, indicating broad adoption.

Skill profile (library / DB)

Skill nature
PROTOCOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
10
Sub-category id
902
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)
Amazon S3 Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Amazon S3 id=170 · amazon-s3

Aliases — catalog

  • Amazon S3 (CANONICAL) primary

Context tags (catalog)

ACL Cross-Region Replication Glacier SSE-KMS SSE-S3 access control bucket bucket policy cross-region replication event notifications lifecycle policy multipart upload object storage pre-signed URL replication static website hosting storage class versioning

Stored enrichment (catalog DB)

Category
Service
Sub-category
Object Storage Service
Vendor
Amazon Web Services
License
proprietary
Year introduced
2006
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Amazon S3 is a standard cloud storage service widely listed in job descriptions and core AWS certifications; it remains a default object-storage choice rather than a niche or sunset product.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
120
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Storage and Data Services Catalog dimension db id 144

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

  • Cloud Storage and File Formats Catalog dimension db id 35

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension saved
Databases Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Orchestration Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Infrastructure Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED

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
Matillion in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Snowflake in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
SQL in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
APIs in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Amazon S3 in_db
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Amazon S3 in_db
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension saved

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed ELT | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Databases | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Orchestration | type=Infrastructure Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
nano JD Parser — gpt-4.1-nano click to toggle
RoleAssociate Data Engineer
CompanyZiff Davis
Experience0–1 years of experience, including internships, certifications, academic projects, or hands-on exposure in Data Engineering
DomainIT Services & Consulting
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "Ziff Davis (NASDAQ: ZD) is",
      "last_5_words": "and Vipre Security."
    },
    "text": "Ziff Davis (NASDAQ: ZD) is a vertically focused digital media and internet company whose portfolio includes leading brands in technology, shopping, gaming and entertainment, connectivity, health, cybersecurity, and martech. Today, Ziff Davis is focused on seven key verticals \u2013 Technology, Connectivity, Shopping, Entertainment, Health \u0026 Wellness, Cybersecurity and Marketing Technology. Its brands include IGN, Mashable, RetailMeNot, PCMag, Humble Bundle, Spiceworks, Ookla (Speedtest), RootMetrics, Everyday Health, BabyCenter, Moz, iContact and Vipre Security.",
    "word_count": 64
  },
  "certifications": [],
  "company_name": "Ziff Davis",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "ITES",
        "BPO",
        "Tech Consulting"
      ],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "BTECH/BE/BSC - Computer Science (or related)",
      "raw": "Bachelor\u2019s degree in Computer Science, Information Technology, or a related field",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": 1,
    "min": 0,
    "raw": "0\u20131 years of experience, including internships, certifications, academic projects, or hands-on exposure in Data Engineering"
  },
  "job_locations": [],
  "role": "Associate Data Engineer",
  "role_aliases": [
    "Data Engineer",
    "Junior Data Engineer",
    "Entry-Level Data Engineer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 11,
      "heading": "Key Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Assist in building and maintaining",
        "last_5_words": "new technologies, tools, and data"
      },
      "text": "Assist in building and maintaining ETL/ELT data pipelines using Matillion or similar tools\nWork with Snowflake to store, process, and analyze data\nWrite, optimize, and maintain SQL queries for large datasets\nPerform data extraction, transformation, and loading from multiple sources (APIs, S3 files, databases)\nMonitor and troubleshoot data workflows and pipelines\nSupport scheduling and automation of jobs using orchestration tools\nEnsure data quality, consistency, and reliability\nCollaborate with team members and stakeholders to understand data requirements\nMaintain proper documentation for data processes and workflows\nWork in a fast-paced, collaborative environment while demonstrating ownership, analytical thinking, and problem-solving skills\nContinuously learn and adapt to new technologies, tools, and data engineering practices",
      "word_count": 139
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "ELT"
    },
    {
      "is_primary": true,
      "skill_name": "Matillion"
    },
    {
      "is_primary": true,
      "skill_name": "Snowflake"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "APIs"
    },
    {
      "is_primary": true,
      "skill_name": "Amazon S3"
    },
    {
      "is_primary": true,
      "skill_name": "Databases"
    },
    {
      "is_primary": true,
      "skill_name": "Orchestration"
    }
  ],
  "jd_role": {
    "display_name": "Associate Data Engineer",
    "rationale": null,
    "role_aliases": [
      "Data Engineer",
      "Junior Data Engineer",
      "Entry-Level Data Engineer"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "Ziff Davis (NASDAQ: ZD) is",
        "last_5_words": "and Vipre Security."
      },
      "text": "Ziff Davis (NASDAQ: ZD) is a vertically focused digital media and internet company whose portfolio includes leading brands in technology, shopping, gaming and entertainment, connectivity, health, cybersecurity, and martech. Today, Ziff Davis is focused on seven key verticals \u2013 Technology, Connectivity, Shopping, Entertainment, Health \u0026 Wellness, Cybersecurity and Marketing Technology. Its brands include IGN, Mashable, RetailMeNot, PCMag, Humble Bundle, Spiceworks, Ookla (Speedtest), RootMetrics, Everyday Health, BabyCenter, Moz, iContact and Vipre Security.",
      "word_count": 64
    },
    "certifications": [],
    "company_name": "Ziff Davis",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "ITES",
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        ],
        "domain": "IT Services \u0026 Consulting"
      },
      "secondary": null
    },
    "education": [
      {
        "level": "Bachelor\u0027s",
        "qualification": "BTECH/BE/BSC - Computer Science (or related)",
        "raw": "Bachelor\u2019s degree in Computer Science, Information Technology, or a related field",
        "requirement": "required"
      }
    ],
    "experience": {
      "max": 1,
      "min": 0,
      "raw": "0\u20131 years of experience, including internships, certifications, academic projects, or hands-on exposure in Data Engineering"
    },
    "job_locations": [],
    "role": "Associate Data Engineer",
    "role_aliases": [
      "Data Engineer",
      "Junior Data Engineer",
      "Entry-Level Data Engineer"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 11,
        "heading": "Key Responsibilities",
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        "source_marker": {
          "first_5_words": "Assist in building and maintaining",
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        },
        "text": "Assist in building and maintaining ETL/ELT data pipelines using Matillion or similar tools\nWork with Snowflake to store, process, and analyze data\nWrite, optimize, and maintain SQL queries for large datasets\nPerform data extraction, transformation, and loading from multiple sources (APIs, S3 files, databases)\nMonitor and troubleshoot data workflows and pipelines\nSupport scheduling and automation of jobs using orchestration tools\nEnsure data quality, consistency, and reliability\nCollaborate with team members and stakeholders to understand data requirements\nMaintain proper documentation for data processes and workflows\nWork in a fast-paced, collaborative environment while demonstrating ownership, analytical thinking, and problem-solving skills\nContinuously learn and adapt to new technologies, tools, and data engineering practices",
        "word_count": 139
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "dab7bda3-791a-45d7-9c0c-8b6285c6df01",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": null,
        "role_id": 2,
        "score": 1.0,
        "slug": "data-engineer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "kra_text": "Implements data quality validation rules, reconciliation checks, and anomaly detection to ensure data completeness, accuracy, and consistency.",
            "sentence": "Ensure data quality, consistency, and reliability",
            "similarity": 0.7035
          },
          {
            "kra_text": "Monitors pipeline health, SLA breach alerts, and job failure notifications, and performs root cause analysis for data pipeline incidents.",
            "sentence": "Monitor and troubleshoot data workflows and pipelines",
            "similarity": 0.6755
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Collaborate with team members and stakeholders to understand data requirements",
            "similarity": 0.6641
          }
        ],
        "matched_count": null,
        "role_id": 2,
        "score": 0.6811,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Ops Engineer",
        "kra_matches": [
          {
            "kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
            "sentence": "Support scheduling and automation of jobs using orchestration tools",
            "similarity": 0.5545
          },
          {
            "kra_text": "Sets up model monitoring dashboards, data drift detection, prediction performance tracking, and alert routing for production ML systems.",
            "sentence": "Monitor and troubleshoot data workflows and pipelines",
            "similarity": 0.5464
          },
          {
            "kra_text": "Validates model performance benchmarks, data schema contracts, and system integration health before signing off on production release readiness.",
            "sentence": "Ensure data quality, consistency, and reliability",
            "similarity": 0.5197
          }
        ],
        "matched_count": null,
        "role_id": 16,
        "score": 0.5402,
        "slug": "ml-ops-engineer",
        "total_count": null
      },
      {
        "display_name": "DevOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
            "sentence": "Monitor and troubleshoot data workflows and pipelines",
            "similarity": 0.6712
          },
          {
            "kra_text": "Manages container orchestration with Kubernetes and Docker, deploying applications as pods, managing namespaces, and configuring auto-scaling across cloud environments.",
            "sentence": "Support scheduling and automation of jobs using orchestration tools",
            "similarity": 0.494
          },
          {
            "kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
            "sentence": "Continuously learn and adapt to new technologies, tools, and data engineering practices",
            "similarity": 0.4481
          }
        ],
        "matched_count": null,
        "role_id": 10,
        "score": 0.5377,
        "slug": "devops-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": [
          {
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            "similarity": 0.579
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          {
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        "matched_count": null,
        "role_id": 3,
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      },
      {
        "display_name": "Full Stack Engineer",
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          },
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            "kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
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    "skill_match_roles": [
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        "role_id": 2,
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      },
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        "display_name": "Cloud Architect",
        "kra_matches": null,
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    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "A",
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      "display_name": "Data Engineer",
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      "role_id": 2,
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      "slug": "data-engineer",
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    },
    "confidence": 0.6811,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Stage 1 title \u0027Data Engineer\u0027 (embedding match, sim 0.74); KRA agrees (0.68)"
  },
  "stage5_updates": {
    "centroid_n_after": 22,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 1856,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1857,
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        "role_slug": "data-engineer",
        "skill_name": "ELT",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1858,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Databases",
        "status": "pending"
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        "is_primary": true,
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        "skill_name": "Orchestration",
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    "v3_pipeline_triggered": false,
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}
API 2 — extract-details
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Databases",
          "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": "databases",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Orchestration",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Infrastructure Tools",
          "skill_nature": "PRACTICE",
          "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": "orchestration",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "ETL",
    "ELT",
    "Databases",
    "Orchestration"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "The primary skills predominantly align with the responsibilities of a Data Engineer.",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "ELT",
      "tag": "new"
    },
    {
      "skill": "Matillion",
      "tag": "in_db"
    },
    {
      "skill": "Snowflake",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "APIs",
      "tag": "in_db"
    },
    {
      "skill": "Amazon S3",
      "tag": "in_db"
    },
    {
      "skill": "Databases",
      "tag": "new"
    },
    {
      "skill": "Orchestration",
      "tag": "new"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "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": "Matillion",
        "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": 118,
        "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": "Snowflake",
        "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": 105,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "SQL",
        "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": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "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": "APIs",
        "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": 1192,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Storage and Data Services",
          "id": 144,
          "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
          "slug": "cloud-storage-and-data-services",
          "source": "db"
        },
        "dimension_id": 144,
        "input_skill": "Amazon S3",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Architect",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 170,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Storage and File Formats",
          "id": 35,
          "rationale": "Object storage and data file formats used as the physical substrate for data movement and lake-style analytics. Data engineers need these to manage landing zones, partitioned datasets, and efficient interchange.",
          "slug": "cloud-storage-and-file-formats",
          "source": "db"
        },
        "dimension_id": 35,
        "input_skill": "Amazon S3",
        "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": 170,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 0
  },
  "planner_output": null,
  "run_id": "dab7bda3-791a-45d7-9c0c-8b6285c6df01"
}

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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