Pipeline run
c47aca82-5b5f-4659-a574-b3a494d1833d
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
• This individual will be responsible for the following: • Lead, coach and guide small teams of analysts to drive innovation, identify risks and gain insights across the bank’s audit risk areas using …
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 is centered on audit analytics, insights, dashboarding, and stakeholder-facing analysis/monitoring rather than deep engineering or BI platform administration.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Job Summary This world class leading Bank is seeking for Audit Analytics Manager for Group Internal Audit’s Audit Analytics function. The function has used analytics and digital to improve the quality of audits and achieve efficiencies. The qualified individual will assist in the execution of analytics strategy and help drive innovation in the function. The function objective is to enhance the department’s data analytics capability, innovate and optimise the use of data throughout the audit cycle including the development of effective continuous monitoring strategies. Responsibilities • This individual will be responsible for the following: • Lead, coach and guide small teams of analysts to drive innovation, identify risks and gain insights across the bank’s audit risk areas using analytics. • Drive innovation, identify risks and gain insights across the bank’s audit risk areas using analytics. • Assist in the governance and development of GIA’s Data Platform consisting of Hadoop based Private Data Lake, Dataiku Servers and Tableau Servers • Contribute to the design, development, testing and documentation of data modelling (includes data sourcing, data preparation, data transformation, and modelling) to assist in population and scoping analysis, sampling, testing, and issue impact justification • Contribute to the design and implementation of effective continuous monitoring and auditing strategies • Work closely with key stakeholders to understand data flow, data points, and advise on how they can be leverage in the design of data analytical solution. • Provide insight and guidance on the interpretation of reported exceptions/analytics outputs so that appropriate actions can be taken by the audit teams • Embrace the agile philosophy and continuously seek feedback for continuous improvement Regulatory & Business Conduct • Display exemplary conduct and live by the Group’s Values and Code of Conduct. • Take personal responsibility for embedding the highest standards of ethics, including regulatory and business conduct, across Standard Chartered Bank. This includes understanding and ensuring compliance with, in letter and spirit, all applicable laws, regulations, guidelines and the Group Code of Conduct. • Effectively and collaboratively identify, escalate, mitigate and resolve risk, conduct and compliance matters. Skills And Experience • Data analysis techniques • Programming fundamentals • Dashboard Design using Tableau • Data Science • Project management and analytical problem-solving skills • Change management and communications • Stakeholder management Qualifications • At least 2 years of experience in leading an analytics team, with minimum of 5 years of experience in a data engineering role preferably with internal audit, a risk function, a control function and/or previous experience in analysing business processes in a financial services environment • Working knowledge of data analysis software; good understanding of data systems and data query and coding language methods, such as SQL, Hive and Python • Experience with working in a Hadoop based Data Lake environment both on premise and on cloud • Experience with metrics and reporting using visualisation tools such as Tableau, PowerBI or similar tools • Good understanding of analytics and techniques, and proven ability to understand problem statements and identify opportunities for use of analytics. • Data Science knowledge would be an advantage (basic understanding of statistics - Correlation, Regression) • Outstanding project management and analytical skills with an ability to multi-task and manage competing priorities with a track record in creative and successful problem-solving • Highly entrepreneurial with a high level of energy, dedication and an unrelenting drive to deliver value, with a track record in creative and successful problem-solving • Outstanding communication skills, both oral and written • Ability to participate within a multi-disciplinary team and be a strong team player • A Bachelor's Degree in Computer Science, Management Information Systems or Business (e.g., Accounting, Business Administration, Economics, Finance, Management, Statistics) is preferred About Standard Chartered We're an international bank, nimble enough to act, big enough for impact. For more than 170 years, we've worked to make a positive difference for our clients, communities, and each other. We question the status quo, love a challenge and enjoy finding new opportunities to grow and do better than before. If you're looking for a career with purpose and you want to work for a bank making a difference, we want to hear from you. You can count on us to celebrate your unique talents and we can't wait to see the talents you can bring us. Our purpose, to drive commerce and prosperity through our unique diversity, together with our brand promise, to be here for good are achieved by how we each live our valued behaviours. When you work with us, you'll see how we value difference and advocate inclusion. Together We • Do the right thing and are assertive, challenge one another, and live with integrity, while putting the client at the heart of what we do • Never settle, continuously striving to improve and innovate, keeping things simple and learning from doing well, and not so well • Are better together, we can be ourselves, be inclusive, see more good in others, and work collectively to build for the long term What We Offer In line with our Fair Pay Charter, we offer a competitive salary and benefits to support your mental, physical, financial and social wellbeing. • Core bank funding for retirement savings, medical and life insurance, with flexible and voluntary benefits available in some locations. • Time-off including annual leave, parental/maternity (20 weeks), sabbatical (12 months maximum) and volunteering leave (3 days), along with minimum global standards for annual and public holiday, which is combined to 30 days minimum. • Flexible working options based around home and office locations, with flexible working patterns. • Proactive wellbeing support through Unmind, a market-leading digital wellbeing platform, development courses for resilience and other human skills, global Employee Assistance Programme, sick leave, mental health first-aiders and all sorts of self-help toolkits • A continuous learning culture to support your growth, with opportunities to reskill and upskill and access to physical, virtual and digital learning. • Being part of an inclusive and values driven organisation, one that embraces and celebrates our unique diversity, across our teams, business functions and geographies - everyone feels respected and can realise their full potential. For further information, and to apply, please visit our website via the “Apply” button below.
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
- 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 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
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Tableau (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Bi Analytics Platform
- Vendor
- Tableau Software
- License
- proprietary
- Year introduced
- 2003
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Tableau appears frequently in BI/data analyst job descriptions and remains a standard enterprise analytics platform with strong vendor support and broad adoption.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 111
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
BI and Visualization Tools Catalog dimension db id 31
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Agile (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Agile
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Agile appears in a large share of software job descriptions and is a standard hiring-pipeline requirement; Scrum/Kanban are commonly listed alongside it, showing broad market adoption.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 3594
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Software Concepts, Patterns & Practices Catalog dimension db id 478
Library dimension (catalog)
Roles linked in library: Engineering Manager
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) |
|
Software Concepts, Patterns & Practices
software-concepts-patterns-practices
|
✓ | — | 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
- Concepts
- 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 |
|---|---|---|---|---|---|---|
| Hadoop | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Tableau | in_db |
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Agile | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Agile | in_db |
Software Concepts, Patterns & Practices
software-concepts-patterns-practices
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Dataiku | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Science | type=Concepts subtype=general nature=CONCEPT lifespan=EVERGREEN |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
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"last_5_words": "you can bring us."
},
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"word_count": 27
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Hadoop"
},
{
"is_primary": true,
"skill_name": "Dataiku"
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{
"is_primary": true,
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{
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},
{
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],
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"kra_match_roles": [
{
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{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Work closely with key stakeholders to understand data flow, data points, and advise on how they can be leverage in the design of data analytical solution.",
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},
{
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},
{
"kra_text": "Designs dimensional models, star schemas, data vault structures, and curated data mart tables to support BI tools and self-service analytics consumption.",
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{
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{
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"sentence": "Effectively and collaboratively identify, escalate, mitigate and resolve risk, conduct and compliance matters.",
"similarity": 0.5454
},
{
"kra_text": "Monitors deployed AI systems for compliance policy drift, regulatory changes, and emerging requirements affecting existing AI deployments.",
"sentence": "Contribute to the design and implementation of effective continuous monitoring and auditing strategies",
"similarity": 0.4609
},
{
"kra_text": "Coordinates AI incident response procedures, regulatory breach notification, audit investigation support, and remediation tracking for compliance issues.",
"sentence": "Provide insight and guidance on the interpretation of reported exceptions/analytics outputs so that appropriate actions can be taken by the audit teams",
"similarity": 0.457
}
],
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},
{
"display_name": "Cyber Security Engineer",
"kra_matches": [
{
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"sentence": "Effectively and collaboratively identify, escalate, mitigate and resolve risk, conduct and compliance matters.",
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{
"kra_text": "Performs threat modeling, security architecture reviews, and quantitative risk analysis for new product features and infrastructure changes.",
"sentence": "Contribute to the design, development, testing and documentation of data modelling (includes data sourcing, data preparation, data transformation, and modelling) to assist in population and scoping analysis, sampling, testing, and issue impact justification",
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},
{
"kra_text": "Performs threat modeling, security architecture reviews, and quantitative risk analysis for new product features and infrastructure changes.",
"sentence": "Lead, coach and guide small teams of analysts to drive innovation, identify risks and gain insights across the bank\u2019s audit risk areas using analytics.",
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}
],
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},
{
"display_name": "MLOps Engineer",
"kra_matches": [
{
"kra_text": "Coordinates model promotion workflows across development, staging, and production environments including integration testing and data contract validation.",
"sentence": "Contribute to the design, development, testing and documentation of data modelling (includes data sourcing, data preparation, data transformation, and modelling) to assist in population and scoping analysis, sampling, testing, and issue impact justification",
"similarity": 0.4879
},
{
"kra_text": "Coordinates model promotion workflows across development, staging, and production environments including integration testing and data contract validation.",
"sentence": "Work closely with key stakeholders to understand data flow, data points, and advise on how they can be leverage in the design of data analytical solution.",
"similarity": 0.4247
},
{
"kra_text": "Supports ML platform incidents by diagnosing model serving failures, feature store pipeline breaks, and training environment configuration issues.",
"sentence": "Assist in the governance and development of GIA\u2019s Data Platform consisting of Hadoop based Private Data Lake, Dataiku Servers and Tableau Servers",
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}
],
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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.",
"sentence": "Work closely with key stakeholders to understand data flow, data points, and advise on how they can be leverage in the design of data analytical solution.",
"similarity": 0.4616
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Contribute to the design, development, testing and documentation of data modelling (includes data sourcing, data preparation, data transformation, and modelling) to assist in population and scoping analysis, sampling, testing, and issue impact justification",
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},
{
"kra_text": "Debugs full-stack issues that span frontend rendering, API behavior, database queries, and infrastructure configuration to identify root causes.",
"sentence": "Provide insight and guidance on the interpretation of reported exceptions/analytics outputs so that appropriate actions can be taken by the audit teams",
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],
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"matched_skills": null,
"role_id": 15,
"score": 0.4449,
"slug": "full-stack-engineer",
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}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
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"matched_count": 2,
"matched_skills": [
"Hadoop",
"Tableau"
],
"role_id": 2,
"score": 0.4,
"slug": "data-engineer",
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},
{
"display_name": "Engineering Manager",
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"matched_count": 1,
"matched_skills": [
"Agile"
],
"role_id": 121,
"score": 0.2,
"slug": "engineering-manager",
"total_count": 5
}
]
},
"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": 0.86,
"slug": "data-analyst",
"total_count": null
},
"confidence": 0.86,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"Audit analytics",
"Risk identification and insight generation",
"Data analysis and reporting",
"Data modeling for audit use cases",
"Continuous monitoring and auditing",
"Stakeholder management",
"Team leadership and coaching",
"Agile delivery"
],
"matched_kras": [
"Lead, coach and guide small teams of analysts",
"Drive innovation, identify risks and gain insights",
"Assist in the governance and development of GIA\u2019s Data Platform",
"Contribute to the design, development, testing and documentation of data modelling",
"Contribute to the design and implementation of effective continuous monitoring and auditing strategies",
"Work closely with key stakeholders to understand data flow",
"Provide insight and guidance on the interpretation of reported exceptions/analytics outputs"
],
"matched_skills": [
"Hadoop",
"Private Data Lake",
"Dataiku",
"Tableau",
"data modelling",
"data sourcing",
"data preparation",
"data transformation",
"sampling",
"continuous monitoring",
"analytics",
"Data Science",
"Programming fundamentals",
"Dashboard Design using Tableau",
"agile philosophy"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Data Engineering \u0026 Analytics; The JD is centered on audit analytics, insights, dashboarding, and stakeholder-facing analysis/monitoring rather than deep engineering or BI platform administration.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 7,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": {
"best_kra_similarity": 0.0,
"queue_id": 1683,
"r_and_r_preview": "\u2022 This individual will be responsible for the following:\n\u2022 Lead, coach and guide small teams of analysts to drive innovation, identify risks and gain insights across the bank\u2019s audit risk areas using ",
"role_display_name": "Data Analyst",
"role_slug": "data-analyst",
"status": "pending"
},
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 22201,
"role_display_name": "Data Analyst",
"role_slug": "data-analyst",
"skill_name": "Dataiku",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 22202,
"role_display_name": "Data Analyst",
"role_slug": "data-analyst",
"skill_name": "Data Science",
"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": 2010,
"existing_alias_text": "Hadoop",
"input_term": "Hadoop",
"matched_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"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 359,
"existing_alias_text": "Tableau",
"input_term": "Tableau",
"matched_canonical": {
"category_id": 9,
"display_name": "Tableau",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "tableau",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 868,
"existing_alias_text": "Agile",
"input_term": "Agile",
"matched_canonical": {
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"is_extractable": true,
"skill_nature": "METHODOLOGY",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
}
],
"candidate_roles": [
{
"display_name": "Data Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
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"display_name": "Engineering Manager",
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"slug": "engineering-manager",
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}
],
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},
"dimensions": [
{
"dimension": {
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},
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{
"display_name": "Data Engineer",
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}
]
},
{
"dimension": {
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},
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{
"display_name": "Data Engineer",
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}
]
},
{
"dimension": {
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"slug": "d_init_01",
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},
"input_skill": "Agile",
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"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Software Concepts, Patterns \u0026 Practices",
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"rationale": "Champion foundational software design patterns, development methodologies, and engineering best practices.",
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},
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}
]
}
],
"input_final_skills": [
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],
"input_llm_skills": [
"Hadoop",
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"Tableau",
"Agile",
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],
"new_aliases_persisted": 0,
"run_id": "c47aca82-5b5f-4659-a574-b3a494d1833d",
"skills_detail": [
{
"aliases_in_db": [
{
"alias_text": "Hadoop",
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"id": 2010,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
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"is_also_category": false,
"is_extractable": true,
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
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"slug": "etl-and-elt-tooling",
"source": "db"
},
"input_skill": "Hadoop",
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"roles_from_db": [
{
"display_name": "Data Engineer",
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"slug": "data-engineer",
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}
]
}
],
"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": [],
"canonical": null,
"dimensions": [],
"input_skill": "Dataiku",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
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"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
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},
"enrichment": null,
"keep_log": [],
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"merge_log": [],
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"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
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{
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}
],
"canonical": {
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},
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"dimension": {
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},
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{
"display_name": "Data Engineer",
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}
]
}
],
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"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": "Agile",
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"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
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"is_also_category": false,
"is_extractable": true,
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
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"display_name": "React Frontend Development",
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"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": "Agile",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
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},
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}
]
}
],
"input_skill": "Agile",
"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": [],
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"dimensions": [],
"input_skill": "Data Science",
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"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Concepts",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "EVERGREEN",
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},
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"warnings": []
},
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"was_in_llm_skills": true
}
],
"unmatched_skills": [
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]
}
API 3 — final-role-output
{
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"rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on audit analytics, insights, dashboarding, and stakeholder-facing analysis/monitoring rather than deep engineering or BI platform administration.",
"role_archetype": null,
"slug": "data-analyst",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Hadoop",
"tag": "in_db"
},
{
"skill": "Dataiku",
"tag": "new"
},
{
"skill": "Tableau",
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},
{
"skill": "Agile",
"tag": "in_db"
},
{
"skill": "Data Science",
"tag": "new"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
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"dimension": {
"difficulty_hint": "well_known",
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"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
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},
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"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": [
{
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}
],
"skill_dimension_saved": true,
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"skill_tag": "in_db",
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},
{
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},
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"input_skill": "Tableau",
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"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",
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"slug": "data-engineer",
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}
],
"skill_dimension_saved": true,
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"skill_tag": "in_db",
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},
{
"chosen_role_id": 143,
"dimension": {
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"slug": "d_init_01",
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},
"dimension_id": 96,
"input_skill": "Agile",
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [],
"skill_dimension_saved": true,
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},
{
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},
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"role_dimension_saved": false,
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"display_name": "Engineering Manager",
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}
],
"skill_dimension_saved": true,
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}
],
"new_skills_created": 0,
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"skill_dimension_saved": 0,
"skipped": 0
},
"planner_output": null,
"run_id": "c47aca82-5b5f-4659-a574-b3a494d1833d"
}