Pipeline run
b4ce403e-4883-403c-88de-661d75afc098
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
Support the execution of overall analytics engagements with a focus on measurement, report automation, & insight development Develop & implement standardized processes for addressing business question…
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Data Analyst
domain · Data Engineering & Analytics CASE DOMAINslug: data-analyst · id: 143 · source: db
Domain=Data Engineering & Analytics; The JD centers on analytics execution, SQL-based analysis, statistical analyses, insight development, and executive presentations, which best matches a Data Analyst role.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Company Description: iProspect is the first truly global digital marketing agency, with 4,300+ employees in 93 offices across 55 countries. A trusted partner with an in-depth understanding of consumer behavior, iProspect reshapes brand strategies to meet the fast-paced demands of the convergent world with a focus on exceeding the client's business objectives. Our global reach, in-depth knowledge of diverse local markets, and expertise produce award-winning, performance-based marketing strategies for leading brands such as General Motors, Hilton, Procter & Gamble, Microsoft, and many others. In 2017, iProspect was named a leader in Forrester Research Inc.'s "The Forrester Wave" Report for search marketing agencies. iProspect has also been listed as the "Best Agency for Performance Marketing" by iMedia three years in a row, MediaPost's 2015 Search Agency of the Year and iMedia's Best Agency for Search. Job Description: The Lead, Analytics is responsible for supporting development & execution of data driven solutions for clients – including report automation, analysis, and insights – with the objective of optimizing the performance of digital marketing initiatives. We are seeking a self-motivated individual who is execution focused, able to deliver under time timelines, & manage data challenges in digital media and marketing. Success in this role requires the ability to manage complexity while collaborating across teams. Key Accountabilities: Support the execution of overall analytics engagements with a focus on measurement, report automation, & insight developmentDevelop & implement standardized processes for addressing business questions through the data capture design & analysesWrite SQL queries to manipulate large data sets & find answersPerform statistical analyses across multiple business units and processesPrepare executive-level presentations for internal and external audiences.Participate in team-wide culture of learning by staying current on current industry trends, developments in methodologies and technologiesReports to Manager, Analytics Qualifications: Two or more years of analytics experience in a marketing or digital-focused roleExcellent analytical and problem-solving skills, coupled with high levels of integrity, autonomy, and self-motivation Excellent written and oral communication skills, including data visualization skillsThe ability to communicate with a range of internal and external partners is essentialTeam-oriented with a collaborative spiritExceptional attention to detail: Business intelligence platform experience (PowerBI, Tableau or Domo preferred)Proficient in MS Office with advanced Excel skillsExperience using SQL will be extremely helpful, Python experience is a plus Additional Information: Dentsu is a modern marketing solutions company. Our mission is to help clients navigate, progress and thrive in a world of change. Businesses rely on our integrated network of agencies and specialized practices to champion meaningful progress through creative, media, commerce, data and technology. Part of Dentsu Group, our global network comprises 66,000 diverse people in 143 countries, who are dedicated to teaming for growth and good. Some of our award-winning agencies include 360i, Carat, dentsumcgarrybowen, DEG, dentsuX, iProspect and Merkle. Follow us on Twitter @DentsuUSA and visit dentsu.com/us. Employees from diverse or underrepresented backgrounds encouraged to apply. Dentsu (the "Company") is committed to a policy of Equal Employment Opportunity and will not discriminate against an applicant or employee of the Company, on the basis of age, sex, sexual orientation, race, color, creed, religion, ethnicity, national origin, alienage or citizenship, disability, marital status, veteran or military status, genetic information, or any other legally-recognized protected basis under federal, state or local laws, regulations or ordinances. Applicants with disabilities may be entitled to reasonable accommodation under the terms of the Americans with Disabilities Act and/or certain state or local laws. A reasonable accommodation is a change in the way things are normally done that will ensure an equal employment opportunity without imposing an undue hardship on the Company. Please contact [email protected] if you need assistance completing any forms or to otherwise participate in the application process or to request or discuss an accommodation in connection with a job at the Company to which you are applying. Apply Now
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
- 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 skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- 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 |
|---|---|---|---|---|---|---|
| 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 skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Statistical Analysis | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=EVERGREEN |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "iProspect is the first truly",
"last_5_words": "iMedia\u0027s Best Agency for Search."
},
"text": "iProspect is the first truly global digital marketing agency, with 4,300+ employees in 93 offices across 55 countries. A trusted partner with an in-depth understanding of consumer behavior, iProspect reshapes brand strategies to meet the fast-paced demands of the convergent world with a focus on exceeding the client\u0027s business objectives. Our global reach, in-depth knowledge of diverse local markets, and expertise produce award-winning, performance-based marketing strategies for leading brands such as General Motors, Hilton, Procter \u0026 Gamble, Microsoft, and many others. In 2017, iProspect was named a leader in Forrester Research Inc.\u0027s \"The Forrester Wave\" Report for search marketing agencies. iProspect has also been listed as the \"Best Agency for Performance Marketing\" by iMedia three years in a row, MediaPost\u0027s 2015 Search Agency of the Year and iMedia\u0027s Best Agency for Search.",
"word_count": 130
},
"certifications": [],
"company_name": "iProspect",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Digital Marketing",
"Marketing Solutions"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": 2,
"raw": "Two or more years of analytics experience in a marketing or digital-focused role"
},
"job_locations": [],
"role": "Lead, Analytics",
"role_aliases": [
"Analytics Lead",
"Lead Analyst",
"Data Analytics Lead"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 6,
"heading": "Key Accountabilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Support the execution of overall",
"last_5_words": "current industry trends, developments in"
},
"text": "Support the execution of overall analytics engagements with a focus on measurement, report automation, \u0026 insight development\nDevelop \u0026 implement standardized processes for addressing business questions through the data capture design \u0026 analyses\nWrite SQL queries to manipulate large data sets \u0026 find answers\nPerform statistical analyses across multiple business units and processes\nPrepare executive-level presentations for internal and external audiences.\nParticipate in team-wide culture of learning by staying current on current industry trends, developments in methodologies and technologies",
"word_count": 66
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "Statistical Analysis"
}
],
"jd_role": {
"display_name": "Lead, Analytics",
"rationale": null,
"role_aliases": [
"Analytics Lead",
"Lead Analyst",
"Data Analytics Lead"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "iProspect is the first truly",
"last_5_words": "iMedia\u0027s Best Agency for Search."
},
"text": "iProspect is the first truly global digital marketing agency, with 4,300+ employees in 93 offices across 55 countries. A trusted partner with an in-depth understanding of consumer behavior, iProspect reshapes brand strategies to meet the fast-paced demands of the convergent world with a focus on exceeding the client\u0027s business objectives. Our global reach, in-depth knowledge of diverse local markets, and expertise produce award-winning, performance-based marketing strategies for leading brands such as General Motors, Hilton, Procter \u0026 Gamble, Microsoft, and many others. In 2017, iProspect was named a leader in Forrester Research Inc.\u0027s \"The Forrester Wave\" Report for search marketing agencies. iProspect has also been listed as the \"Best Agency for Performance Marketing\" by iMedia three years in a row, MediaPost\u0027s 2015 Search Agency of the Year and iMedia\u0027s Best Agency for Search.",
"word_count": 130
},
"certifications": [],
"company_name": "iProspect",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Digital Marketing",
"Marketing Solutions"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": 2,
"raw": "Two or more years of analytics experience in a marketing or digital-focused role"
},
"job_locations": [],
"role": "Lead, Analytics",
"role_aliases": [
"Analytics Lead",
"Lead Analyst",
"Data Analytics Lead"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 6,
"heading": "Key Accountabilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Support the execution of overall",
"last_5_words": "current industry trends, developments in"
},
"text": "Support the execution of overall analytics engagements with a focus on measurement, report automation, \u0026 insight development\nDevelop \u0026 implement standardized processes for addressing business questions through the data capture design \u0026 analyses\nWrite SQL queries to manipulate large data sets \u0026 find answers\nPerform statistical analyses across multiple business units and processes\nPrepare executive-level presentations for internal and external audiences.\nParticipate in team-wide culture of learning by staying current on current industry trends, developments in methodologies and technologies",
"word_count": 66
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "b4ce403e-4883-403c-88de-661d75afc098",
"stage3_signals": {
"alias_found": false,
"alias_match_roles": [],
"kra_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": [
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Develop \u0026 implement standardized processes for addressing business questions through the data capture design \u0026 analyses",
"similarity": 0.5751
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Support the execution of overall analytics engagements with a focus on measurement, report automation, \u0026 insight development",
"similarity": 0.473
},
{
"kra_text": "Optimizes pipeline throughput, partitioning strategies, and query performance across cloud data warehouses like Snowflake, BigQuery, or Redshift.",
"sentence": "Write SQL queries to manipulate large data sets \u0026 find answers",
"similarity": 0.441
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.4964,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Java Backend Developer",
"kra_matches": [
{
"kra_text": "persistence and data modeling",
"sentence": "Develop \u0026 implement standardized processes for addressing business questions through the data capture design \u0026 analyses",
"similarity": 0.4982
},
{
"kra_text": "backend performance tuning",
"sentence": "Write SQL queries to manipulate large data sets \u0026 find answers",
"similarity": 0.4179
},
{
"kra_text": "Server-side business logic implementation",
"sentence": "Perform statistical analyses across multiple business units and processes",
"similarity": 0.407
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 79,
"score": 0.4411,
"slug": "java-backend-developer",
"total_count": null
},
{
"display_name": "Svelte Frontend Developer",
"kra_matches": [
{
"kra_text": "backend data integration",
"sentence": "Develop \u0026 implement standardized processes for addressing business questions through the data capture design \u0026 analyses",
"similarity": 0.4549
},
{
"kra_text": "backend data integration",
"sentence": "Write SQL queries to manipulate large data sets \u0026 find answers",
"similarity": 0.4008
},
{
"kra_text": "backend data integration",
"sentence": "Perform statistical analyses across multiple business units and processes",
"similarity": 0.395
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 92,
"score": 0.4169,
"slug": "svelte-frontend-developer",
"total_count": null
},
{
"display_name": "Fullstack Developer",
"kra_matches": [
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Write SQL queries to manipulate large data sets \u0026 find answers",
"similarity": 0.5131
},
{
"kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
"sentence": "Develop \u0026 implement standardized processes for addressing business questions through the data capture design \u0026 analyses",
"similarity": 0.3788
},
{
"kra_text": "Debugs full-stack issues that span frontend rendering, API behavior, database queries, and infrastructure configuration to identify root causes.",
"sentence": "Support the execution of overall analytics engagements with a focus on measurement, report automation, \u0026 insight development",
"similarity": 0.3493
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 15,
"score": 0.4138,
"slug": "full-stack-engineer",
"total_count": null
},
{
"display_name": "MLOps Engineer",
"kra_matches": [
{
"kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
"sentence": "Support the execution of overall analytics engagements with a focus on measurement, report automation, \u0026 insight development",
"similarity": 0.4413
},
{
"kra_text": "Validates model performance benchmarks, data schema contracts, and system integration health before signing off on production release readiness.",
"sentence": "Develop \u0026 implement standardized processes for addressing business questions through the data capture design \u0026 analyses",
"similarity": 0.4117
},
{
"kra_text": "Coordinates model promotion workflows across development, staging, and production environments including integration testing and data contract validation.",
"sentence": "Perform statistical analyses across multiple business units and processes",
"similarity": 0.3865
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 16,
"score": 0.4131,
"slug": "ml-ops-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"SQL"
],
"role_id": 2,
"score": 0.5,
"slug": "data-engineer",
"total_count": 2
},
{
"display_name": "Pega Developer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"SQL"
],
"role_id": 24,
"score": 0.5,
"slug": "pega-developer",
"total_count": 2
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
"display_name": "Data Analyst",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 143,
"score": 0.92,
"slug": "data-analyst",
"total_count": null
},
"confidence": 0.92,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"Analytics Engagement Execution",
"Data Analysis and Insight Development",
"Business Question Analysis",
"Executive Reporting and Presentation"
],
"matched_kras": [
"Support the execution of overall analytics engagements",
"Develop \u0026 implement standardized processes for addressing business questions",
"Write SQL queries to manipulate large data sets",
"Perform statistical analyses across multiple business units and processes",
"Prepare executive-level presentations for internal and external audiences"
],
"matched_skills": [
"measurement",
"report automation",
"SQL",
"statistical analyses"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Data Engineering \u0026 Analytics; The JD centers on analytics execution, SQL-based analysis, statistical analyses, insight development, and executive presentations, which best matches a Data Analyst role.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 2,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": {
"best_kra_similarity": 0.0,
"queue_id": 536,
"r_and_r_preview": "Support the execution of overall analytics engagements with a focus on measurement, report automation, \u0026 insight development\nDevelop \u0026 implement standardized processes for addressing business question",
"role_display_name": "Data Analyst",
"role_slug": "data-analyst",
"status": "pending"
},
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 8861,
"role_display_name": "Data Analyst",
"role_slug": "data-analyst",
"skill_name": "Statistical Analysis",
"status": "pending"
}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
"alias_matches": [
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 271,
"existing_alias_text": "SQL",
"input_term": "SQL",
"matched_canonical": {
"category_id": 6,
"display_name": "SQL",
"id": 101,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "sql",
"sub_category_id": 97,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
}
],
"candidate_roles": [
{
"display_name": "Pega Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"chosen_role": {
"display_name": "Data Analyst",
"id": 143,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on analytics execution, SQL-based analysis, statistical analyses, insight development, and executive presentations, which best matches a Data Analyst role.",
"role_archetype": null,
"slug": "data-analyst",
"source": "db"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Pega Programming Languages \u0026 DSLs",
"id": 267,
"rationale": "Programming languages and domain-specific languages used in Pega development.",
"slug": "pega-programming-languages-dsls",
"source": "db"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Pega Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_final_skills": [
"SQL",
"Statistical Analysis"
],
"input_llm_skills": [
"SQL",
"Statistical Analysis"
],
"new_aliases_persisted": 0,
"run_id": "b4ce403e-4883-403c-88de-661d75afc098",
"skills_detail": [
{
"aliases_in_db": [
{
"alias_text": "SQL",
"alias_type": "CANONICAL",
"id": 271,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 6,
"display_name": "SQL",
"id": 101,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "sql",
"sub_category_id": 97,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Pega Programming Languages \u0026 DSLs",
"id": 267,
"rationale": "Programming languages and domain-specific languages used in Pega development.",
"slug": "pega-programming-languages-dsls",
"source": "db"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Pega Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "SQL",
"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": "Statistical Analysis",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "statistical-analysis",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Statistical Analysis"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Analyst",
"id": 143,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on analytics execution, SQL-based analysis, statistical analyses, insight development, and executive presentations, which best matches a Data Analyst role.",
"role_archetype": null,
"slug": "data-analyst",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "Statistical Analysis",
"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": 143,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Pega Programming Languages \u0026 DSLs",
"id": 267,
"rationale": "Programming languages and domain-specific languages used in Pega development.",
"slug": "pega-programming-languages-dsls",
"source": "db"
},
"dimension_id": 267,
"input_skill": "SQL",
"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": "Pega Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 101,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 143,
"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": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 101,
"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": "b4ce403e-4883-403c-88de-661d75afc098"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.