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Pipeline run

9ef4abac-b90c-4d83-bdb2-7f0c6148fcc9

Pipeline LLM cost (USD)

API 1: $0.0025 API 2: $0.0000 API 3: $0.0000 Total: $0.0025

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description

SPARSE JD

Nature of work

—

no_db_connection

Tech stack maturity

Mainstream Modern

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 (3)

Python SQL Apache Spark

Skill cluster (0 dimension groups, role-scoped)

No dimension groups computed for this JD.

Show KRA description ↓

- Python and SQL for pipelines - Apache Spark processing - Experience with FluxNine Orchestrator for pipeline scheduling

Status: jd_deconstruction_v4_done Created: 2026-08-13T07:34:41.516492Z Updated: 2026-08-13T08:42:45.075724Z Run duration: 7406 ms

Flow v4 catalog deconstruction skill_library_v4

1 LLM mention extraction + deterministic catalog sweep

2 Six-layer resolution cascade (exact → alias → fuzzy → embedding → judge → AI-prediction lane)

3 Table-lookup layering: dim_members → role_dim_map tiers, served verbatim

Role Chosen role & resolution

Data Engineer

in_db name family · data-engineer

slug: data-engineer · role_id: 1839a54d-e909-543e-b36e-745551d4e000 · catalog: skill_library_v4 (tiers served verbatim from role_dim_map)

The role is for a Data Engineer, requiring proficiency in Python and SQL for developing data pipelines, as well as experience with Apache Spark for data processing. A distinctive requirement is familiarity with FluxNine Orchestrator for scheduling these pipelines. The job context does not specify a particular company or domain.

Job description

Job Title: Data EngineerRequirements:- Python and SQL for pipelines- Apache Spark processing- Experience with FluxNine Orchestrator for pipeline scheduling

Skills from this JD

L0–L3 tiers come verbatim from the role's role_dim_map; violet AI GENERATED chips are AI-prediction-lane skills pending review in the queue.

L0 · Anchor 3 skill(s)

Python Programming Languages the craft is exercised in Python and SQL; every owned artifact — transform, DAG, quality check — is authored in them.

SQL Programming Languages the craft is exercised in Python and SQL; every owned artifact — transform, DAG, quality check — is authored in them.

Apache Spark Data Processing & Pipeline Frameworks transformation engines are where pipeline work physically happens; Spark and dataframe fluency is the single strongest resume signal for the

Consider adding canon exemplars the JD missed

Apache Beam — Data Processing & Pipeline Frameworks (L0)

Scala — Programming Languages (L0)

Prefect — Workflow Orchestration (L0)

Google Cloud Storage — Cloud Platforms (L1)

Airbyte — Data Ingestion & Integration (L1)

Apache Parquet — Data Lake & Storage Formats (L1)

Unmapped not resolved, not classified — quarantined for review

FluxNine Orchestrator

Library artifacts (this run)

No artifact rows for this run.

nano JD Parser — gpt-4.1-nano click to toggle

RoleData Engineer

DomainOther

JD type pass

Show raw JSON

{
  "JD_type": "pass",
  "about_company": null,
  "ai_kras": [],
  "certifications": [],
  "client_details": null,
  "company_name": null,
  "company_size": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [],
      "domain": "Other"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": null,
    "raw": null
  },
  "job_locations": [],
  "notice_period": {
    "days": null,
    "raw": null
  },
  "open_to_relocate": false,
  "role": "Data Engineer",
  "role_aliases": [
    {
      "name": "Data Engineer",
      "reasoning": "generalized form of the picked role",
      "relation": "synonym"
    }
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 3,
      "heading": "Requirements",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "- Python and SQL for",
        "last_5_words": "for pipeline scheduling"
      },
      "text": "- Python and SQL for pipelines\n- Apache Spark processing\n- Experience with FluxNine Orchestrator for pipeline scheduling",
      "word_count": 19
    }
  ],
  "urls": []
}

API 1 — extract-from-jd click to toggle

{
  "catalog": "v4",
  "consider_adding": [
    {
      "dimension": "Data Processing \u0026 Pipeline Frameworks",
      "reason": "In the role canon\u0027s Data Processing \u0026 Pipeline Frameworks - adding it narrows the candidate pool.",
      "skill": "Apache Beam",
      "tier": "L0"
    },
    {
      "dimension": "Programming Languages",
      "reason": "In the role canon\u0027s Programming Languages - adding it narrows the candidate pool.",
      "skill": "Scala",
      "tier": "L0"
    },
    {
      "dimension": "Workflow Orchestration",
      "reason": "In the role canon\u0027s Workflow Orchestration - adding it narrows the candidate pool.",
      "skill": "Prefect",
      "tier": "L0"
    },
    {
      "dimension": "Cloud Platforms",
      "reason": "In the role canon\u0027s Cloud Platforms - adding it narrows the candidate pool.",
      "skill": "Google Cloud Storage",
      "tier": "L1"
    },
    {
      "dimension": "Data Ingestion \u0026 Integration",
      "reason": "In the role canon\u0027s Data Ingestion \u0026 Integration - adding it narrows the candidate pool.",
      "skill": "Airbyte",
      "tier": "L1"
    },
    {
      "dimension": "Data Lake \u0026 Storage Formats",
      "reason": "In the role canon\u0027s Data Lake \u0026 Storage Formats - adding it narrows the candidate pool.",
      "skill": "Apache Parquet",
      "tier": "L1"
    }
  ],
  "final_skills": [
    {
      "dimension": {
        "display_name": "Programming Languages",
        "slug": "programming-languages"
      },
      "is_primary": true,
      "layer": "L0",
      "layer_source": "catalog_v4",
      "origin": "catalog",
      "rationale": "the craft is exercised in Python and SQL; every owned artifact \u2014 transform, DAG, quality check \u2014 is authored in them.",
      "skill_id": "646cb350-7c71-5ed2-971e-d141c566ce6e",
      "skill_name": "Python"
    },
    {
      "dimension": {
        "display_name": "Programming Languages",
        "slug": "programming-languages"
      },
      "is_primary": true,
      "layer": "L0",
      "layer_source": "catalog_v4",
      "origin": "catalog",
      "rationale": "the craft is exercised in Python and SQL; every owned artifact \u2014 transform, DAG, quality check \u2014 is authored in them.",
      "skill_id": "dd6b38b8-6e50-5e89-a827-5b03688e6113",
      "skill_name": "SQL"
    },
    {
      "dimension": {
        "display_name": "Data Processing \u0026 Pipeline Frameworks",
        "slug": "data-processing-pipeline-frameworks"
      },
      "is_primary": true,
      "layer": "L0",
      "layer_source": "catalog_v4",
      "origin": "catalog",
      "rationale": "transformation engines are where pipeline work physically happens; Spark and dataframe fluency is the single strongest resume signal for the",
      "skill_id": "f885334e-9062-5967-bc62-b5b5a27664cf",
      "skill_name": "Apache Spark"
    }
  ],
  "history_run_id": null,
  "jd_parameters": {
    "certifications": [],
    "clientDetails": null,
    "company": null,
    "companySize": null,
    "ctc": {
      "currency": null,
      "max": null,
      "min": null,
      "period": null,
      "raw": null
    },
    "educationRequirements": [],
    "experience": {
      "max": null,
      "min": null,
      "raw": null
    },
    "industryDomain": "Other",
    "knockouts": {
      "certifications": [],
      "ctc": {
        "currency": null,
        "max": null,
        "min": null
      },
      "educationRequirements": [],
      "experience": {
        "max": null,
        "min": null
      },
      "location": [],
      "noticePeriod": null
    },
    "locations": [],
    "noticePeriod": null,
    "openToRelocate": false,
    "role": "Data Engineer",
    "roleSynonyms": [
      "Data Engineer"
    ]
  },
  "jd_summary": "The role is for a Data Engineer, requiring proficiency in Python and SQL for developing data pipelines, as well as experience with Apache Spark for data processing. A distinctive requirement is familiarity with FluxNine Orchestrator for scheduling these pipelines. The job context does not specify a particular company or domain.",
  "layer_conflicts": [],
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "ai_kras": [],
    "certifications": [],
    "client_details": null,
    "company_name": null,
    "company_size": null,
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [],
        "domain": "Other"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": null,
      "min": null,
      "raw": null
    },
    "job_locations": [],
    "notice_period": {
      "days": null,
      "raw": null
    },
    "open_to_relocate": false,
    "role": "Data Engineer",
    "role_aliases": [
      {
        "name": "Data Engineer",
        "reasoning": "generalized form of the picked role",
        "relation": "synonym"
      }
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 3,
        "heading": "Requirements",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "- Python and SQL for",
          "last_5_words": "for pipeline scheduling"
        },
        "text": "- Python and SQL for pipelines\n- Apache Spark processing\n- Experience with FluxNine Orchestrator for pipeline scheduling",
        "word_count": 19
      }
    ],
    "urls": []
  },
  "pipeline": "v4",
  "rejected": false,
  "rejection_code": null,
  "rejection_reason": null,
  "role": {
    "canonical_name": "Data Engineer",
    "family": "data-engineer",
    "match_method": "name",
    "resolution": "in_db",
    "role_id": "1839a54d-e909-543e-b36e-745551d4e000",
    "similarity": null,
    "slug": "data-engineer"
  },
  "run_id": "jdv4-a96dccb035f6",
  "secondary_meta": [],
  "secondary_skills": [],
  "skill_layers": [
    {
      "label": "Anchor",
      "layer": "L0",
      "skills": [
        {
          "dimension": {
            "display_name": "Programming Languages",
            "slug": "programming-languages"
          },
          "name": "Python",
          "origin": "catalog",
          "rationale": "the craft is exercised in Python and SQL; every owned artifact \u2014 transform, DAG, quality check \u2014 is authored in them."
        },
        {
          "dimension": {
            "display_name": "Programming Languages",
            "slug": "programming-languages"
          },
          "name": "SQL",
          "origin": "catalog",
          "rationale": "the craft is exercised in Python and SQL; every owned artifact \u2014 transform, DAG, quality check \u2014 is authored in them."
        },
        {
          "dimension": {
            "display_name": "Data Processing \u0026 Pipeline Frameworks",
            "slug": "data-processing-pipeline-frameworks"
          },
          "name": "Apache Spark",
          "origin": "catalog",
          "rationale": "transformation engines are where pipeline work physically happens; Spark and dataframe fluency is the single strongest resume signal for the"
        }
      ]
    }
  ],
  "unmapped_skills": [
    "FluxNine Orchestrator"
  ]
}

API 2 — extract-details

{}

API 3 — final-role-output

{}

LLM Calls

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

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