Pipeline run
5a326e44-ba9b-40e8-b3d5-67427acf8cff
Pipeline LLM cost (USD)
API 1: $0.0038
API 2: $0.0005
API 3: $0.0000
Total: $0.0043
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 transformation and modeling
Build and support batch/near-real-time data pipelines and warehouse solutions using DataStage, cloud ETL, and T-SQL, while modeling data, working on OLAP schemas, and leading junior engineers on transformation projects.
"Develop, test and support end-to-end batch and near real-time data flows/pipelines."
Tech stack maturity
Mainstream Modern
A data engineer focused on analytics typically works with established modern data platforms and workflows rather than legacy-only or bleeding-edge stacks.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.20 / 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):
AI, Artificial Intelligence
Evidence — skills matched in JD (16)
DataStage
ETL
T-SQL
Data Warehousing
OLAP
Batch Processing
Real-Time Data Processing
Data Architecture
Big Data
Analytics
Cloud Platforms
Data Governance
Information Management
Data Modeling
Business Systems Design
Data Transformation
Skill cluster (1 dimension groups, role-scoped)
Cross-cutting / unaligned
DataStage
ETL
T-SQL
Data Warehousing
OLAP
Batch Processing
Real-Time Data Processing
Data Architecture
Big Data
Analytics
Cloud Platforms
Data Governance
Information Management
Data Modeling
Business Systems Design
Data Transformation
Show KRA description ↓
• Develop, test and support future-ready data solutions for customers across industry verticals.
• Develop, test, and support end-to-end batch and near real-time data flows/pipelines.
• Demonstrate understanding of data architectures, modern data platforms, big data, analytics, cloud platforms, data governance and information management and associated technologies.
• Communicates risks and ensures understanding of these risks.
• Graduate with a minimum of 5+ years of related experience required.
• Experience in modelling and business system designs.
• Good hands-on experience on DataStage, Cloud-based ETL Services.
• Have great expertise in writing TSQL code.
• Well-versed with data warehouse schemas and OLAP techniques.
• Ability to manage and make decisions about competing priorities and resources.
• Ability to delegate where appropriate.
• Must be a strong team player/leader.
• Ability to lead Data transformation projects with multiple junior data engineers.
• Strong oral written and interpersonal skills for interacting throughout all levels of the organization.
• Ability to communicate complex business problems and technical solutions.
Signals
Skill
—
—
Alias
data-engineer
1.00
KRA
data-engineer
0.64
Post-classification
Centroidupdated · n=463
Alias collision log—
New-role queue—
New skills captured15
New KRA captured—
Captured for admin review
DataStage
primary
↔
Data Engineer
pending
ETL
primary
↔
Data Engineer
pending
T-SQL
primary
↔
Data Engineer
pending
Data Warehousing
primary
↔
Data Engineer
pending
OLAP
primary
↔
Data Engineer
pending
Batch Processing
primary
↔
Data Engineer
pending
Real-Time Data Processing
primary
↔
Data Engineer
pending
Data Architecture
primary
↔
Data Engineer
pending
Big Data
primary
↔
Data Engineer
pending
Cloud Platforms
primary
↔
Data Engineer
pending
Data Governance
primary
↔
Data Engineer
pending
Information Management
primary
↔
Data Engineer
pending
Data Modeling
primary
↔
Data Engineer
pending
Business Systems Design
primary
↔
Data Engineer
pending
Data Transformation
primary
↔
Data Engineer
pending
Status:
completed
Created: 2026-05-27T16:43:43.930631Z
Updated: 2026-05-27T16:45:16.751580Z
API 3 duration: 3280 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 Aslug: data-engineer · id: 2 · source: db
Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top absent does not contradict
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
3
Skipped
Job description
Introduction A career in IBM Consulting embraces long-term relationships and close collaboration with clients across the globe. In this role, you will work for IBM BPO, part of Consulting that, accelerates digital transformation using agile methodologies, process mining, and AI-powered workflows. You'll work with visionaries across multiple industries to improve the hybrid cloud and AI journey for the most innovative and valuable companies in the world. Your ability to accelerate impact and make meaningful change for your clients is enabled by our strategic partner ecosystem and our robust technology platforms across the IBM portfolio, including IBM Software and Red Hat. Curiosity and a constant quest for knowledge serve as the foundation to success in IBM Consulting. In your role, you'll be supported by mentors and coaches who will encourage you to challenge the norm, investigate ideas outside of your role, and come up with creative solutions resulting in groundbreaking impact for a wide network of clients. Our culture of evolution and empathy centers on long-term career growth and learning opportunities in an environment that embraces your unique skills and experience. Your role and responsibilities • Develop, test and support future-ready data solutions for customers across industry verticals. • Develop, test, and support end-to-end batch and near real-time data flows/pipelines. • Demonstrate understanding of data architectures, modern data platforms, big data, analytics, cloud platforms, data governance and information management and associated technologies. • Communicates risks and ensures understanding of these risks. • Graduate with a minimum of 5+ years of related experience required. • Experience in modelling and business system designs. • Good hands-on experience on DataStage, Cloud-based ETL Services. • Have great expertise in writing TSQL code. • Well-versed with data warehouse schemas and OLAP techniques. Required education Bachelor's Degree Preferred Education Master's Degree Required technical and professional expertise • Ability to manage and make decisions about competing priorities and resources. • Ability to delegate where appropriate. • Must be a strong team player/leader. • Ability to lead Data transformation projects with multiple junior data engineers. • Strong oral written and interpersonal skills for interacting throughout all levels of the organization. • Ability to communicate complex business problems and technical solutions. About Business Unit IBM Consulting is IBM’s consulting and global professional services business, with market leading capabilities in business and technology transformation. With deep expertise in many industries, we offer strategy, experience, technology, and operations services to many of the most innovative and valuable companies in the world. Our people are focused on accelerating our clients’ businesses through the power of collaboration. We believe in the power of technology responsibly used to help people, partners and the planet. YOUR LIFE @ IBM In a world where technology never stands still, we understand that, dedication to our clients success, innovation that matters, and trust and personal responsibility in all our relationships, lives in what we do as IBMers as we strive to be the catalyst that makes the world work better. Being an IBMer means you’ll be able to learn and develop yourself and your career, you’ll be encouraged to be courageous and experiment everyday, all whilst having continuous trust and support in an environment where everyone can thrive whatever their personal or professional background. Our IBMers are growth minded, always staying curious, open to feedback and learning new information and skills to constantly transform themselves and our company. They are trusted to provide on-going feedback to help other IBMers grow, as well as collaborate with colleagues keeping in mind a team focused approach to include different perspectives to drive exceptional outcomes for our customers. The courage our IBMers have to make critical decisions everyday is essential to IBM becoming the catalyst for progress, always embracing challenges with resources they have to hand, a can-do attitude and always striving for an outcome focused approach within everything that they do. Are you ready to be an IBMer? About Ibm IBM’s greatest invention is the IBMer. We believe that through the application of intelligence, reason and science, we can improve business, society and the human condition, bringing the power of an open hybrid cloud and AI strategy to life for our clients and partners around the world. Restlessly reinventing since 1911, we are not only one of the largest corporate organizations in the world, we’re also one of the biggest technology and consulting employers, with many of the Fortune 50 companies relying on the IBM Cloud to run their business. At IBM, we pride ourselves on being an early adopter of artificial intelligence, quantum computing and blockchain. Now it’s time for you to join us on our journey to being a responsible technology innovator and a force for good in the world. IBM is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status. Other Relevant Job Details When applying to jobs of your interest, we recommend that you do so for those that match your experience and expertise. Our recruiters advise that you apply to not more than 3 roles in a year for the best candidate experience. For additional information about location requirements, please discuss with the recruiter following submission of your application.
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
DataStage
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
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
T-SQL
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
- Programming Languages
- Sub-category
- general
- Skill nature
- LANGUAGE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Data Warehousing
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
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
OLAP
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
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Batch Processing
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
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Real-Time Data Processing
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
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Data Architecture
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
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Big Data
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
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Analytics
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: Analytics
id=1664
· analytics
Aliases — catalog
- Analytics (CANONICAL)
Context tags (catalog)
A/B testing
ETL
KPI
Python
R
SQL
business intelligence
dashboards
data mining
data storytelling
data visualization
data warehousing
machine learning
predictive modeling
statistical analysis
Stored enrichment (catalog DB)
- Category
- Domain
- Sub-category
- Analytics
- Confidence
- 0.94
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Analytics appears in a large share of data, product, and BI job descriptions, and major vendors (Google Analytics, Adobe Analytics, Power BI) continue to invest heavily in the category.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 37
- Sub-category id
- 1257
- 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) |
Cloud Platforms
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
- Cloud Platforms
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Data Governance
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
- Information Management
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Information Management
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
- Information Management
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Data Modeling
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: domain modeling
id=2379
· domain-modeling
Aliases — catalog
- domain modeling (CANONICAL) primary
- Domain Modeling (CANONICAL)
Context tags (catalog)
CQRS
DDD
ERD
UML
aggregate
bounded context
business logic
context map
context mapping
data modeling
domain events
domain-driven design
entities
entity
event sourcing
event storming
microservices
repositories
repository pattern
service layer
services
value object
value objects
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Domain Modeling
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in software JDs under DDD/business analysis; many roles ask for domain modeling or domain-driven design, and it remains a standard design skill rather than a niche tool.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 2831
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Application Architecture Patterns Catalog dimension db id 293
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Python Backend Developer
-
Service Architecture and Design Patterns Catalog dimension db id 18
Library dimension (catalog)
Roles linked in library: Backend Developer, Java Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, PHP Backend Developer, Ruby Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Application Architecture Patterns
application-architecture-patterns
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
|
Service Architecture and Design Patterns
service-architecture-and-design-patterns
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Business Systems Design
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
- Information Management
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Data Transformation
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: Data Transform
id=1890
· data-transform
Aliases — catalog
- Data Transform (CANONICAL) primary
Context tags (catalog)
ETL
batch processing
data aggregation
data cleansing
data integration
data lineage
data mapping
data modeling
data pipeline
data quality
data visualization
data wrangling
real-time processing
schema evolution
transformation logic
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Transformation Concept
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Data transformation is a core ETL/ELT concept and appears broadly across job descriptions for analytics, data engineering, and BI roles; it’s a standard pipeline requirement rather than a niche tool.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1445
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Data Pages and Data Modeling Catalog dimension db id 254
Library dimension (catalog)
Roles linked in library: Pega Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Data Pages and Data Modeling
data-pages-and-data-modeling
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
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 |
|---|---|---|---|---|---|---|
| Analytics | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Data Modeling | new |
Application Architecture Patterns
application-architecture-patterns
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Data Modeling | new |
Service Architecture and Design Patterns
service-architecture-and-design-patterns
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Data Transformation | new |
Data Pages and Data Modeling
data-pages-and-data-modeling
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | DataStage | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ETL | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | T-SQL | type=Programming Languages subtype=general nature=LANGUAGE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Warehousing | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | OLAP | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Batch Processing | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Real-Time Data Processing | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Architecture | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Big Data | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Cloud Platforms | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Governance | type=Information Management subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Information Management | type=Information Management subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Business Systems Design | type=Information Management subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Data Modeling ↔ Application Architecture Patterns | |
| dimension_skill_link_proposed | Data Modeling ↔ Service Architecture and Design Patterns | |
| dimension_skill_link_proposed | Data Transformation ↔ Data Pages and Data Modeling |
nano JD Parser — gpt-4.1-nano click to toggle
RoleData Engineer
CompanyIBM
Experienceminimum of 5+ years of related experience required
DomainIT Services & Consulting
JD type
pass
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "IBM\u2019s greatest invention is the",
"last_5_words": "to life for our clients."
},
"text": "IBM\u2019s greatest invention is the IBMer. We believe that through the application of intelligence, reason and science, we can improve business, society and the human condition, bringing the power of an open hybrid cloud and AI strategy to life for our clients and partners around the world.",
"word_count": 48
},
"certifications": [],
"company_name": "IBM",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Any Discipline",
"raw": "Bachelor\u0027s Degree",
"requirement": "required"
},
{
"level": "Master\u0027s",
"qualification": "MTECH/ME/MSC - Any Discipline",
"raw": "Master\u0027s Degree",
"requirement": "preferred"
}
],
"experience": {
"max": null,
"min": 5,
"raw": "minimum of 5+ years of related experience required"
},
"job_locations": [],
"role": "Data Engineer",
"role_aliases": [
"Data Developer",
"ETL Developer",
"Data Solutions Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 9,
"heading": "Your role and responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Develop, test and support",
"last_5_words": "schemas and OLAP techniques."
},
"text": "\u2022 Develop, test and support future-ready data solutions for customers across industry verticals.\n\u2022 Develop, test, and support end-to-end batch and near real-time data flows/pipelines.\n\u2022 Demonstrate understanding of data architectures, modern data platforms, big data, analytics, cloud platforms, data governance and information management and associated technologies.\n\u2022 Communicates risks and ensures understanding of these risks.\n\u2022 Graduate with a minimum of 5+ years of related experience required.\n\u2022 Experience in modelling and business system designs.\n\u2022 Good hands-on experience on DataStage, Cloud-based ETL Services.\n\u2022 Have great expertise in writing TSQL code.\n\u2022 Well-versed with data warehouse schemas and OLAP techniques.",
"word_count": 108
},
{
"bullet_count": 6,
"heading": "Required technical and professional expertise",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Ability to manage and",
"last_5_words": "business problems and technical solutions."
},
"text": "\u2022 Ability to manage and make decisions about competing priorities and resources.\n\u2022 Ability to delegate where appropriate.\n\u2022 Must be a strong team player/leader.\n\u2022 Ability to lead Data transformation projects with multiple junior data engineers.\n\u2022 Strong oral written and interpersonal skills for interacting throughout all levels of the organization.\n\u2022 Ability to communicate complex business problems and technical solutions.",
"word_count": 66
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "DataStage"
},
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": true,
"skill_name": "T-SQL"
},
{
"is_primary": true,
"skill_name": "Data Warehousing"
},
{
"is_primary": true,
"skill_name": "OLAP"
},
{
"is_primary": true,
"skill_name": "Batch Processing"
},
{
"is_primary": true,
"skill_name": "Real-Time Data Processing"
},
{
"is_primary": true,
"skill_name": "Data Architecture"
},
{
"is_primary": true,
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}
API 2 — extract-details
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},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "T-SQL",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Programming Languages",
"skill_nature": "LANGUAGE",
"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": "t-sql",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Warehousing",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Databases",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
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"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "data-warehousing",
"split_log": [],
"typed": null,
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},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "OLAP",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Databases",
"skill_nature": "CONCEPT",
"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": "olap",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Batch Processing",
"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": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "batch-processing",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Real-Time Data Processing",
"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": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "real-time-data-processing",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Architecture",
"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": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "data-architecture",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Big Data",
"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": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "big-data",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Analytics",
"alias_type": "CANONICAL",
"id": 2634,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 37,
"display_name": "Analytics",
"id": 1664,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "analytics",
"sub_category_id": 1257,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Analytics",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Analytics",
"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": "Cloud Platforms",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Cloud Platforms",
"skill_nature": "PLATFORM",
"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": "cloud-platforms",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Governance",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Information Management",
"skill_nature": "CONCEPT",
"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": "data-governance",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Information Management",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Information Management",
"skill_nature": "CONCEPT",
"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": "information-management",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "domain modeling",
"alias_type": "CANONICAL",
"id": 3675,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Domain Modeling",
"alias_type": "CANONICAL",
"id": 5644,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "domain modeling",
"id": 2379,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "domain-modeling",
"sub_category_id": 2831,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Application Architecture Patterns",
"id": 293,
"rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
"slug": "application-architecture-patterns",
"source": "db"
},
"input_skill": "Data Modeling",
"llm_role": null,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Service Architecture and Design Patterns",
"id": 18,
"rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
"slug": "service-architecture-and-design-patterns",
"source": "db"
},
"input_skill": "Data Modeling",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "Data Modeling",
"matched_via": "embedding_alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Business Systems Design",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Information Management",
"skill_nature": "CONCEPT",
"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": "business-systems-design",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Data Transform",
"alias_type": "CANONICAL",
"id": 2894,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "Data Transform",
"id": 1890,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "data-transform",
"sub_category_id": 1445,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Data Pages and Data Modeling",
"id": 254,
"rationale": "Defines how Pega applications source, shape, and expose data for cases and UI components. This includes declarative data access, parameterized data pages, and the data objects used to support process execution.",
"slug": "data-pages-and-data-modeling",
"source": "db"
},
"input_skill": "Data Transformation",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Pega Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
}
]
}
],
"input_skill": "Data Transformation",
"matched_via": "embedding_alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"DataStage",
"ETL",
"T-SQL",
"Data Warehousing",
"OLAP",
"Batch Processing",
"Real-Time Data Processing",
"Data Architecture",
"Big Data",
"Cloud Platforms",
"Data Governance",
"Information Management",
"Business Systems Design"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top absent does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "DataStage",
"tag": "new"
},
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "T-SQL",
"tag": "new"
},
{
"skill": "Data Warehousing",
"tag": "new"
},
{
"skill": "OLAP",
"tag": "new"
},
{
"skill": "Batch Processing",
"tag": "new"
},
{
"skill": "Real-Time Data Processing",
"tag": "new"
},
{
"skill": "Data Architecture",
"tag": "new"
},
{
"skill": "Big Data",
"tag": "new"
},
{
"skill": "Analytics",
"tag": "in_db"
},
{
"skill": "Cloud Platforms",
"tag": "new"
},
{
"skill": "Data Governance",
"tag": "new"
},
{
"skill": "Information Management",
"tag": "new"
},
{
"skill": "Data Modeling",
"tag": "in_db"
},
{
"skill": "Business Systems Design",
"tag": "new"
},
{
"skill": "Data Transformation",
"tag": "in_db"
}
],
"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": "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": "Analytics",
"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": 1664,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Application Architecture Patterns",
"id": 293,
"rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
"slug": "application-architecture-patterns",
"source": "db"
},
"dimension_id": 293,
"input_skill": "Data Modeling",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Service Architecture and Design Patterns",
"id": 18,
"rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
"slug": "service-architecture-and-design-patterns",
"source": "db"
},
"dimension_id": 18,
"input_skill": "Data Modeling",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Data Pages and Data Modeling",
"id": 254,
"rationale": "Defines how Pega applications source, shape, and expose data for cases and UI components. This includes declarative data access, parameterized data pages, and the data objects used to support process execution.",
"slug": "data-pages-and-data-modeling",
"source": "db"
},
"dimension_id": 254,
"input_skill": "Data Transformation",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"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": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 3
},
"planner_output": null,
"run_id": "5a326e44-ba9b-40e8-b3d5-67427acf8cff"
}