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Pipeline run

25c9310e-7eb8-4c1a-8463-ba59b32a0ec4

Pipeline LLM cost (USD)
API 1: $0.0046 API 2: $0.0006 API 3: $0.0000 Total: $0.0053

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 platform optimization and governance
Build and operate SAP-based cloud data platforms, integrating SAP Datasphere/BDC/HANA with Oracle, Kafka, Snowflake and S3, while designing secure, automated, monitored data solutions and supporting data mesh/governance.
""Understand business processes, select best approaches, evaluate their performance and assess business relevance to implement for Data mesh, data modelling and data governance.""
Tech stack maturity
Mainstream Modern
The stack centers on widely adopted modern data engineering technologies like Kafka, Snowflake, S3, MySQL/PostgreSQL, and observability tools, without indicating highly cloud-native or bleeding-edge architecture.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
1.70 / 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): embeddings, MLOps, AI, ML, Machine Learning, Artificial Intelligence
Evidence — skills matched in JD (28)
SAP Datasphere SAP Business Data Cloud SAP Analytics Cloud SLT Replication Server SAP HANA Cloud Oracle Kafka MySQL PostgreSQL Snowflake Amazon S3 Cloud Native Data Integration Data Management Data Mesh Data Modeling Data Governance Data Science Artificial Intelligence Machine Learning SAP AI Core Security Monitoring Logging Alerting +3
Skill cluster (8 dimension groups, role-scoped)
Relational Database Usage
MySQL PostgreSQL
AI Governance and Model Security
Machine Learning
Cloud Data Warehouses
Snowflake
Cloud Storage and File Formats
Amazon S3
Messaging and Event Streaming
Kafka
Observability and Diagnostics
Logging
Observability and Incident Response
Alerting
Cross-cutting / unaligned
SAP Datasphere SAP Business Data Cloud SAP Analytics Cloud SLT Replication Server SAP HANA Cloud Oracle Cloud Native Data Integration Data Management Data Mesh Data Modeling Data Governance Data Science Artificial Intelligence SAP AI Core Security Monitoring Automation SAP S/4HANA Predictive Analytics
Show KRA description ↓
• As a Data Platform Engineer you are responsible for: Engage and deliver solutions using SAP Data Management Products like SAP Datasphere, SAP Business Data Cloud (SAP BDC) , SAP Analytics Cloud, SLT Replication Server, SAP Hana Cloud. • Responsible for deliver data integration solutions in complex hybrid landscape where multiple Non-SAP technologies exist like Oracle, Kafka, MySQL, Postgres, Snowflake, S3, etc. • Engage in Data Platform administration and operational activities enabled in our platform. • Architect, Design and implement cloud native solutions • Engage in Data Preparation, Data Integration, Data Management and preparation tasks in BDC/Datasphere. • Understand business processes, select best approaches, evaluate their performance and assess business relevance to implement for Data mesh, data modelling and Data governance. • Support in preparation of Data Science, AI, ML Embedded in S/4, AI Core, Snowflake and other solutions. • Expand own skill sets to other and upcoming areas of AI, ML, Predictive Analytics of SAP • Analytical, results-driven, and have a solution-oriented approach. Work as a team player yet can work independently. • Interpersonal and communication skills including verbal, written presentation, and listening. • Implement and design best practices for security concepts that are essential for cloud native applications. • Collaborate with cross-functional teams to design and implement secure and robust data engineering architectures for performance, scalability, and cost-efficiency. • Understands and uses monitoring, logging, and alerting solutions to continuously assess and improve system reliability, and performance. • Have passion to drive automation to streamline manual processes and enhance productivity across the organization. • Stay up-to-date with emerging technologies, industry trends, and best practices in SAP Data Management, Data Engineering and Cloud Data Solutions.

Signals

Skill php-backend-developer
0.19
Alias data-engineer
1.00
KRA data-engineer
0.58

Post-classification

Centroidupdated · n=404
Alias collision log
New-role queue
New skills captured18
New KRA captured

Captured for admin review

SAP Datasphere primary Data Engineer pending
SAP Business Data Cloud primary Data Engineer pending
SAP Analytics Cloud primary Data Engineer pending
SLT Replication Server primary Data Engineer pending
SAP HANA Cloud primary Data Engineer pending
Oracle primary Data Engineer pending
Cloud Native primary Data Engineer pending
Data Integration primary Data Engineer pending
Data Management primary Data Engineer pending
Data Mesh primary Data Engineer pending
Data Modeling primary Data Engineer pending
Data Governance primary Data Engineer pending
Data Science primary Data Engineer pending
SAP AI Core primary Data Engineer pending
Predictive Analytics Data Engineer pending
Security primary Data Engineer pending
Automation primary Data Engineer pending
SAP S/4HANA primary Data Engineer pending
Status: completed Created: 2026-05-27T16:08:59.251885Z Updated: 2026-05-27T16:11:15.662565Z API 3 duration: 48516 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Data Engineer

CASE A

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

Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top php-backend-developer 0.19 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
2
Skipped

Job description

Summary

Are you seeking an environment where you can drive innovation? Does the prospect of working with top engineering talent get you charged up? Apple is a place where extraordinary people gather to do their best work. Together we create products and experiences people once couldn’t have imagined — and now can’t imagine living without.

Apple’s IS&T manages key business and technical infrastructure at Apple -- how online orders are placed, the customer experience with technology in our retail stores, how much network capacity we need around the world and much more. The SAP Global Systems team within IS&T runs the Operations and Financial transactional platform that powers all of Apple functions like Sales, Manufacturing, Distribution and Financials. Think platform-as-product! Our team is responsible for enabling Platforms in technology domains Data Management and AI. We define solution patterns together with business and other IS&T stakeholders with our technical expertise of SAP and Cloud Products. In the area of Data Management, What we build is a scalable, well defined data landscape that allows our business stakeholders to consume the data that they want, when they want and to move to an insight driven business decision process. We use the right technologies to power our decision-making and also continuously align our platforms with technical innovations as well as overall cooperate strategy.

Description

As a SAP and Cloud Data Platform Engineer at Apple, you will be a key contributor to the design, development, and operation of our next-generation Data platform. You will work alongside a team of talented engineers to build a highly scalable, reliable, and secure platform that empowers Apple's product teams to deliver world-class experiences. You will be responsible for driving innovation, adopting new technologies, and ensuring the platform meets the evolving needs of Apple's business.

Responsibilities

• As a Data Platform Engineer you are responsible for: Engage and deliver solutions using SAP Data Management Products like SAP Datasphere, SAP Business Data Cloud (SAP BDC) , SAP Analytics Cloud, SLT Replication Server, SAP Hana Cloud. 
• Responsible for deliver data integration solutions in complex hybrid landscape where multiple Non-SAP technologies exist like Oracle, Kafka, MySQL, Postgres, Snowflake, S3, etc.
• Engage in Data Platform administration and operational activities enabled in our platform.
• Architect, Design and implement cloud native solutions
• Engage in Data Preparation, Data Integration, Data Management and preparation tasks in BDC/Datasphere.
• Understand business processes, select best approaches, evaluate their performance and assess business relevance to implement for Data mesh, data modelling and Data governance.
• Support in preparation of Data Science, AI, ML Embedded in S/4, AI Core, Snowflake and other solutions.
• Expand own skill sets to other and upcoming areas of AI, ML, Predictive Analytics of SAP
• Analytical, results-driven, and have a solution-oriented approach. Work as a team player yet can work independently.
• Interpersonal and communication skills including verbal, written presentation, and listening.
• Implement and design best practices for security concepts that are essential for cloud native applications.
• Collaborate with cross-functional teams to design and implement secure and robust data engineering architectures for performance, scalability, and cost-efficiency.
• Understands and uses monitoring, logging, and alerting solutions to continuously assess and improve system reliability, and performance.
• Have passion to drive automation to streamline manual processes and enhance productivity across the organization.
• Stay up-to-date with emerging technologies, industry trends, and best practices in SAP Data Management, Data Engineering and Cloud Data Solutions.


Minimum Qualifications

• 7-10 years of Experience in the relevant field.
• At least 2 projects exposed as Data Warehouse, Data Analysis, modeler, data integration
• Understanding of SAP BTP.
• Experience in SAP Data & analytics landscape, SAP Datasphere/ SAP Business Data Cloud (BDC), SAP Analytics Cloud (SAC).
• Data solutioning skills like Hana Data modeling, Data transformation, SQL Scripting, Performance evaluation and optimization.
• Data Integration Experience with Replication, federation, SAP CDC, scheduling/monitoring with SAP and Non-SAP data technologies.
• SAP Analytics Cloud expertise to implement solutions around Stories, Dashboards, Planning, etc.
• Experience in building connections , SQL/graphical views, replication / data flows
• Experience on CDS views, embedded analytics, integration patterns
• Open SQL schema consumption patterns for external compute/ML notebooks
• A strong sense of ownership, good critical thinking & interpersonal skills to work successfully across diverse business and technical & multi-functional teams.


Preferred Qualifications

• Bachelor degree in Computer Science, Engineering or other relevant major
• Python + Hana- ML integration; optional AI Core for ML ops, chunking, embeddings, vectors
• Strong understanding of common authentication schemes, certificates, secrets and protocols.
• Scripting and automation - Python for data/ML workflows against HANA Cloud/Datasphere (hdbcli, hana-ml, JDBC/ODBC).
• Expertise on SAP BW 7.x and BW/4HANA, S/4HANA.
• Orchestration/monitoring patterns for data pipelines.
• Understand complex landscape architectures. Have working knowledge of on-prem and cloud based hybrid architectures and infrastructure concepts of Regions, Availability Zones, VPCs/Subnets, Loadbalancers, API Gateways etc.
• Experience in Client/ Customer/Business stake holders interaction
• Coordination support for important issues (investigating, providing workarounds) for complex and cross-component issues

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

SAP Datasphere 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
SAP Business Data Cloud 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
SAP Analytics Cloud 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
SLT Replication Server 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
SAP HANA Cloud 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
Oracle Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Kafka Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Kafka id=36 · kafka

Aliases — catalog

  • Kafka (CANONICAL) primary

Context tags (catalog)

Apache Flink Apache Kafka Apache Pulsar Apache Spark Avro KSQL Kafka API Kafka Connect Kafka Streams ZooKeeper Zookeeper backpressure brokers consumer consumer group consumer groups event sourcing event-driven architecture exactly-once semantics fault tolerance high throughput log compaction message broker message queue microservices offsets partition partitioning partitions producer producer API real-time analytics real-time data replication schema registry stream processing topic topic partitioning topics

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Event Stream Store
Vendor
Confluent
License
apache_2
Year introduced
2011
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Kafka appears in many production JDs for event streaming and data pipelines, and remains a standard platform in cloud/vendor offerings (e.g., Confluent, AWS MSK), indicating broad hiring demand.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
3533
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Asynchronous Messaging and Event Streaming Catalog dimension db id 297

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Go Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Scala Backend Developer

  • Messaging and Background Jobs Catalog dimension db id 291

    Library dimension (catalog)

    Roles linked in library: PHP Backend Developer, Python Backend Developer, Ruby Backend Developer

  • Messaging and Event Streaming Catalog dimension db id 8

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Messaging and Background Jobs
messaging-and-background-jobs
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension saved
MySQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: MySQL id=17 · mysql

Aliases — catalog

  • MySQL (CANONICAL) primary

Context tags (catalog)

ACID ACID compliance Backup Data Warehousing Database Design ER Diagrams EXPLAIN Indexes InnoDB JOIN Joins MyISAM Normalization PHPMyAdmin Query Optimization Replication SQL SQL queries Stored Procedures Transactions Triggers backup backup and restore backup strategies data modeling data normalization database design database normalization database schema foreign keys indexes indexing joins master-slave normalization performance tuning query optimization read replicas replication schema design schema management sharding stored procedures transaction isolation transaction management transactions triggers

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database
Vendor
Oracle Corporation
License
gpl_v2
Year introduced
1995
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: MySQL appears in a large share of backend/DB job descriptions and remains a standard managed offering across AWS RDS, Cloud SQL, and Azure Database, indicating broad hiring-pipeline demand.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
29
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

    Roles linked in library: Fullstack Developer, Fullstack Developer, PHP Backend Developer

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer

  • Relational Database Usage Catalog dimension db id 371

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: PostgreSQL id=16 · postgresql

Aliases — catalog

  • PostgreSQL (CANONICAL) primary
  • PG 13 (VERSION)
  • PG 14 (VERSION)
  • PG 15 (VERSION)
  • PG 16 (VERSION)
  • PostgreSQL 13 (VERSION)
  • PostgreSQL 14 (VERSION)
  • PostgreSQL 15 (VERSION)
  • PostgreSQL 16 (VERSION)
  • Postgres 13 (VERSION)
  • Postgres 14 (VERSION)
  • Postgres 15 (VERSION)
  • Postgres 16 (VERSION)
  • pg10 (VERSION)
  • pg11 (VERSION)
  • pg12 (VERSION)
  • pg13 (VERSION)
  • pg14 (VERSION)
  • pg15 (VERSION)
  • pg16 (VERSION)
  • postgres (VERSION)
  • postgresql 10 (VERSION)
  • postgresql 11 (VERSION)
  • postgresql 12 (VERSION)
  • postgresql 13 (VERSION)
  • postgresql 14 (VERSION)
  • postgresql 15 (VERSION)
  • postgresql 16 (VERSION)
  • postgresql-16 (VERSION)
  • postgresql10 (VERSION)
  • postgresql11 (VERSION)
  • postgresql12 (VERSION)
  • postgresql13 (VERSION)
  • postgresql14 (VERSION)
  • postgresql15 (VERSION)
  • postgresql16 (VERSION)

Context tags (catalog)

ACID EXPLAIN JSONB PL/pgSQL PostGIS SQL VACUUM backup data integrity database migration extensions indexes indexing joins migration partitioning performance tuning pgAdmin query optimization replication schema stored procedures table partitioning transaction transactions triggers views

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database
Vendor
PostgreSQL Global Development Group
License
other_open
Year introduced
1996
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: PostgreSQL appears in a large share of backend/data engineering job postings and is a default managed option across AWS RDS, GCP Cloud SQL, and Azure Database, indicating broad hiring-pipeline adoption.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
29
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

    Roles linked in library: Fullstack Developer, Fullstack Developer, PHP Backend Developer

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer

  • Relational Database Usage Catalog dimension db id 371

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Snowflake Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Snowflake id=105 · snowflake

Aliases — catalog

  • Snowflake (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
113
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Data Warehouses Catalog dimension db id 22

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
Amazon S3 Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Amazon S3 id=170 · amazon-s3

Aliases — catalog

  • Amazon S3 (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Cloud Storage and Data Services Catalog dimension db id 144

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

  • Cloud Storage and File Formats Catalog dimension db id 35

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension saved
Cloud Native 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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Integration Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data 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
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Mesh 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
Concepts
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
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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Science 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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Artificial Intelligence Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Artificial Intelligence id=1357 · artificial-intelligence

Aliases — catalog

  • Artificial Intelligence (CANONICAL)

Context tags (catalog)

AI ethics PyTorch TensorFlow algorithm optimization computer vision data mining deep learning machine learning model training natural language processing neural networks predictive analytics reinforcement learning supervised learning unsupervised learning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Artificial Intelligence
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: AI appears in a large and growing share of job descriptions across software, data, and product roles, and major vendors (Microsoft, Google, AWS) have standardized AI offerings, signaling broad market adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1020
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)
Machine Learning Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Machine Learning id=1356 · machine-learning

Aliases — catalog

  • Machine Learning (CANONICAL)

Context tags (catalog)

Keras PyTorch TensorFlow cross-validation data preprocessing ensemble methods feature engineering hyperparameter tuning model evaluation natural language processing neural networks reinforcement learning scikit-learn supervised learning unsupervised learning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Machine Learning
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Machine Learning appears in large volumes of job descriptions across data, product, and platform roles, and major cloud vendors (AWS, Google Cloud, Azure) offer dedicated ML services and certifications, indicating broad adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1024
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • AI Governance and Model Security Catalog dimension db id 50

    Library dimension (catalog)

    Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SAP AI Core 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
Machine Learning Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Predictive Analytics Secondary 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
Machine Learning Frameworks
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Security 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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Monitoring Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Monitoring id=1218 · monitoring

Aliases — catalog

  • Monitoring (CANONICAL)

Context tags (catalog)

ELK Stack Grafana Prometheus SLI SLO alerting anomaly detection dashboards health checks incident response logging metrics monitoring as code observability tracing

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Observability Monitoring
Confidence
0.88
Version strategy
NOT_APPLICABLE

Maturity reasoning: Monitoring is a standard requirement in most SRE/DevOps job descriptions and is bundled into major platforms like AWS CloudWatch, Datadog, and Prometheus, indicating broad market adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
924
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Observability and Incident Triage Catalog dimension db id 155

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Observability and Incident Triage
observability-and-incident-triage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Logging Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: logging id=1624 · logging

Aliases — catalog

  • logging (CANONICAL) primary
  • Logging (CANONICAL)

Context tags (catalog)

ELK stack Grafana Prometheus Splunk alerting audit logs audit trails centralized logging debugging distributed tracing event logging log aggregation log analysis log correlation log levels log management log monitoring log parsing log retention log visualization observability performance metrics real-time logging structured logging tracing

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Observability Concept
Confidence
0.92
Version strategy
NOT_APPLICABLE

Maturity reasoning: Logging is a standard requirement in most software JDs and observability stacks; it appears alongside metrics/tracing in mainstream tooling like ELK, Splunk, and CloudWatch rather than as a niche specialty.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
752
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Observability and Diagnostics Catalog dimension db id 287

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer, Java Backend Developer, Python Backend Developer

  • Observability and Incident Response Catalog dimension db id 10

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Node.js Backend Developer, PHP Backend Developer

  • Observability and Operations Catalog dimension db id 143

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Observability and Diagnostics
observability-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Alerting Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: alerting id=882 · alerting

Aliases — catalog

  • alerting (CANONICAL) primary

Context tags (catalog)

Grafana SLA SLA compliance SLAs SLIs SLOs alert fatigue alert management alert prioritization alerting frameworks alerting policies alerting rules alerting systems alertmanager anomaly detection dashboard dashboards escalation policies grafana incident response log analysis metrics monitoring notifications observability prometheus real-time alerts root cause analysis thresholds webhooks

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Alerting
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Alerting is a standard SRE/DevOps requirement and appears in many JDs alongside Prometheus, Grafana, PagerDuty, and Datadog; vendors actively market alerting features rather than sunsetting them.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
3472
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Backend Observability, Logging, and Diagnostics Catalog dimension db id 388

    Library dimension (catalog)

    Roles linked in library: Kotlin Backend Developer, Scala Backend Developer

  • Observability and Incident Response Catalog dimension db id 10

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Node.js Backend Developer, PHP Backend Developer

  • Observability and Incident Triage Catalog dimension db id 155

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Incident Triage
observability-and-incident-triage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Automation 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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
SAP S/4HANA 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

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
Kafka in_db
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kafka in_db
Messaging and Background Jobs
messaging-and-background-jobs
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kafka in_db
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension saved
MySQL in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MySQL in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MySQL in_db
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Snowflake in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
Amazon S3 in_db
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Amazon S3 in_db
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension saved
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
Artificial Intelligence in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning in_db
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Monitoring in_db
Observability and Incident Triage
observability-and-incident-triage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Logging in_db
Observability and Diagnostics
observability-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Logging in_db
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Logging in_db
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Alerting in_db
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Alerting in_db
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Alerting in_db
Observability and Incident Triage
observability-and-incident-triage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed SAP Datasphere | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed SAP Business Data Cloud | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed SAP Analytics Cloud | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed SLT Replication Server | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed SAP HANA Cloud | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Oracle | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Cloud Native | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Integration | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Management | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Mesh | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Governance | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Science | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed SAP AI Core | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Predictive Analytics | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Security | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Automation | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed SAP S/4HANA | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
dimension_skill_link_proposed Data Modeling ↔ Application Architecture Patterns
dimension_skill_link_proposed Data Modeling ↔ Service Architecture and Design Patterns
nano JD Parser — gpt-4.1-nano click to toggle
RoleSAP and Cloud Data Platform Engineer
CompanyApple
Experience7-10 years of Experience in the relevant field.
DomainIT Services & Consulting
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "Are you seeking an environment",
      "last_5_words": "overall cooperate strategy."
    },
    "text": "Are you seeking an environment where you can drive innovation? Does the prospect of working with top engineering talent get you charged up? Apple is a place where extraordinary people gather to do their best work. Together we create products and experiences people once couldn\u2019t have imagined \u2014 and now can\u2019t imagine living without.\n\nApple\u2019s IS\u0026T manages key business and technical infrastructure at Apple -- how online orders are placed, the customer experience with technology in our retail stores, how much network capacity we need around the world and much more. The SAP Global Systems team within IS\u0026T runs the Operations and Financial transactional platform that powers all of Apple functions like Sales, Manufacturing, Distribution and Financials. Think platform-as-product! Our team is responsible for enabling Platforms in technology domains Data Management and AI. We define solution patterns together with business and other IS\u0026T stakeholders with our technical expertise of SAP and Cloud Products. In the area of Data Management, What we build is a scalable, well defined data landscape that allows our business stakeholders to consume the data that they want, when they want and to move to an insight driven business decision process. We use the right technologies to power our decision-making and also continuously align our platforms with technical innovations as well as overall cooperate strategy.",
    "word_count": 236
  },
  "certifications": [],
  "company_name": "Apple",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "Tech Consulting",
        "SAP Solutions"
      ],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "BTECH/BE - Computer Science (or relevant)",
      "raw": "Bachelor degree in Computer Science, Engineering or other relevant major",
      "requirement": "preferred"
    }
  ],
  "experience": {
    "max": 10,
    "min": 7,
    "raw": "7-10 years of Experience in the relevant field."
  },
  "job_locations": [],
  "role": "SAP and Cloud Data Platform Engineer",
  "role_aliases": [
    "Data Platform Engineer",
    "Cloud Data Engineer",
    "SAP Data Engineer"
  ],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 13,
      "heading": "Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 As a Data Platform Engineer",
        "last_5_words": "Data Management, Data Engineering and Cloud Data Solutions."
      },
      "text": "\u2022 As a Data Platform Engineer you are responsible for: Engage and deliver solutions using SAP Data Management Products like SAP Datasphere, SAP Business Data Cloud (SAP BDC) , SAP Analytics Cloud, SLT Replication Server, SAP Hana Cloud.\n\u2022 Responsible for deliver data integration solutions in complex hybrid landscape where multiple Non-SAP technologies exist like Oracle, Kafka, MySQL, Postgres, Snowflake, S3, etc.\n\u2022 Engage in Data Platform administration and operational activities enabled in our platform.\n\u2022 Architect, Design and implement cloud native solutions\n\u2022 Engage in Data Preparation, Data Integration, Data Management and preparation tasks in BDC/Datasphere.\n\u2022 Understand business processes, select best approaches, evaluate their performance and assess business relevance to implement for Data mesh, data modelling and Data governance.\n\u2022 Support in preparation of Data Science, AI, ML Embedded in S/4, AI Core, Snowflake and other solutions.\n\u2022 Expand own skill sets to other and upcoming areas of AI, ML, Predictive Analytics of SAP\n\u2022 Analytical, results-driven, and have a solution-oriented approach. Work as a team player yet can work independently.\n\u2022 Interpersonal and communication skills including verbal, written presentation, and listening.\n\u2022 Implement and design best practices for security concepts that are essential for cloud native applications.\n\u2022 Collaborate with cross-functional teams to design and implement secure and robust data engineering architectures for performance, scalability, and cost-efficiency.\n\u2022 Understands and uses monitoring, logging, and alerting solutions to continuously assess and improve system reliability, and performance.\n\u2022 Have passion to drive automation to streamline manual processes and enhance productivity across the organization.\n\u2022 Stay up-to-date with emerging technologies, industry trends, and best practices in SAP Data Management, Data Engineering and Cloud Data Solutions.",
      "word_count": 335
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "SAP Datasphere"
    },
    {
      "is_primary": true,
      "skill_name": "SAP Business Data Cloud"
    },
    {
      "is_primary": true,
      "skill_name": "SAP Analytics Cloud"
    },
    {
      "is_primary": true,
      "skill_name": "SLT Replication Server"
    },
    {
      "is_primary": true,
      "skill_name": "SAP HANA Cloud"
    },
    {
      "is_primary": true,
      "skill_name": "Oracle"
    },
    {
      "is_primary": true,
      "skill_name": "Kafka"
    },
    {
      "is_primary": true,
      "skill_name": "MySQL"
    },
    {
      "is_primary": true,
      "skill_name": "PostgreSQL"
    },
    {
      "is_primary": true,
      "skill_name": "Snowflake"
    },
    {
      "is_primary": true,
      "skill_name": "Amazon S3"
    },
    {
      "is_primary": true,
      "skill_name": "Cloud Native"
    },
    {
      "is_primary": true,
      "skill_name": "Data Integration"
    },
    {
      "is_primary": true,
      "skill_name": "Data Management"
    },
    {
      "is_primary": true,
      "skill_name": "Data Mesh"
    },
    {
      "is_primary": true,
      "skill_name": "Data Modeling"
    },
    {
      "is_primary": true,
      "skill_name": "Data Governance"
    },
    {
      "is_primary": true,
      "skill_name": "Data Science"
    },
    {
      "is_primary": true,
      "skill_name": "Artificial Intelligence"
    },
    {
      "is_primary": true,
      "skill_name": "Machine Learning"
    },
    {
      "is_primary": true,
      "skill_name": "SAP AI Core"
    },
    {
      "is_primary": false,
      "skill_name": "Predictive Analytics"
    },
    {
      "is_primary": true,
      "skill_name": "Security"
    },
    {
      "is_primary": true,
      "skill_name": "Monitoring"
    },
    {
      "is_primary": true,
      "skill_name": "Logging"
    },
    {
      "is_primary": true,
      "skill_name": "Alerting"
    },
    {
      "is_primary": true,
      "skill_name": "Automation"
    },
    {
      "is_primary": true,
      "skill_name": "SAP S/4HANA"
    }
  ],
  "jd_role": {
    "display_name": "SAP and Cloud Data Platform Engineer",
    "rationale": null,
    "role_aliases": [
      "Data Platform Engineer",
      "Cloud Data Engineer",
      "SAP Data Engineer"
    ],
    "role_archetype": "Engineering",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "Are you seeking an environment",
        "last_5_words": "overall cooperate strategy."
      },
      "text": "Are you seeking an environment where you can drive innovation? Does the prospect of working with top engineering talent get you charged up? Apple is a place where extraordinary people gather to do their best work. Together we create products and experiences people once couldn\u2019t have imagined \u2014 and now can\u2019t imagine living without.\n\nApple\u2019s IS\u0026T manages key business and technical infrastructure at Apple -- how online orders are placed, the customer experience with technology in our retail stores, how much network capacity we need around the world and much more. The SAP Global Systems team within IS\u0026T runs the Operations and Financial transactional platform that powers all of Apple functions like Sales, Manufacturing, Distribution and Financials. Think platform-as-product! Our team is responsible for enabling Platforms in technology domains Data Management and AI. We define solution patterns together with business and other IS\u0026T stakeholders with our technical expertise of SAP and Cloud Products. In the area of Data Management, What we build is a scalable, well defined data landscape that allows our business stakeholders to consume the data that they want, when they want and to move to an insight driven business decision process. We use the right technologies to power our decision-making and also continuously align our platforms with technical innovations as well as overall cooperate strategy.",
      "word_count": 236
    },
    "certifications": [],
    "company_name": "Apple",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "Tech Consulting",
          "SAP Solutions"
        ],
        "domain": "IT Services \u0026 Consulting"
      },
      "secondary": null
    },
    "education": [
      {
        "level": "Bachelor\u0027s",
        "qualification": "BTECH/BE - Computer Science (or relevant)",
        "raw": "Bachelor degree in Computer Science, Engineering or other relevant major",
        "requirement": "preferred"
      }
    ],
    "experience": {
      "max": 10,
      "min": 7,
      "raw": "7-10 years of Experience in the relevant field."
    },
    "job_locations": [],
    "role": "SAP and Cloud Data Platform Engineer",
    "role_aliases": [
      "Data Platform Engineer",
      "Cloud Data Engineer",
      "SAP Data Engineer"
    ],
    "role_archetype": "Engineering",
    "roles_and_responsibilities": [
      {
        "bullet_count": 13,
        "heading": "Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 As a Data Platform Engineer",
          "last_5_words": "Data Management, Data Engineering and Cloud Data Solutions."
        },
        "text": "\u2022 As a Data Platform Engineer you are responsible for: Engage and deliver solutions using SAP Data Management Products like SAP Datasphere, SAP Business Data Cloud (SAP BDC) , SAP Analytics Cloud, SLT Replication Server, SAP Hana Cloud.\n\u2022 Responsible for deliver data integration solutions in complex hybrid landscape where multiple Non-SAP technologies exist like Oracle, Kafka, MySQL, Postgres, Snowflake, S3, etc.\n\u2022 Engage in Data Platform administration and operational activities enabled in our platform.\n\u2022 Architect, Design and implement cloud native solutions\n\u2022 Engage in Data Preparation, Data Integration, Data Management and preparation tasks in BDC/Datasphere.\n\u2022 Understand business processes, select best approaches, evaluate their performance and assess business relevance to implement for Data mesh, data modelling and Data governance.\n\u2022 Support in preparation of Data Science, AI, ML Embedded in S/4, AI Core, Snowflake and other solutions.\n\u2022 Expand own skill sets to other and upcoming areas of AI, ML, Predictive Analytics of SAP\n\u2022 Analytical, results-driven, and have a solution-oriented approach. Work as a team player yet can work independently.\n\u2022 Interpersonal and communication skills including verbal, written presentation, and listening.\n\u2022 Implement and design best practices for security concepts that are essential for cloud native applications.\n\u2022 Collaborate with cross-functional teams to design and implement secure and robust data engineering architectures for performance, scalability, and cost-efficiency.\n\u2022 Understands and uses monitoring, logging, and alerting solutions to continuously assess and improve system reliability, and performance.\n\u2022 Have passion to drive automation to streamline manual processes and enhance productivity across the organization.\n\u2022 Stay up-to-date with emerging technologies, industry trends, and best practices in SAP Data Management, Data Engineering and Cloud Data Solutions.",
        "word_count": 335
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "25c9310e-7eb8-4c1a-8463-ba59b32a0ec4",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
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    ],
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          },
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        "display_name": "Cloud Security Engineer",
        "kra_matches": [
          {
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      },
      {
        "display_name": "Cloud Architect",
        "kra_matches": [
          {
            "kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
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            "similarity": 0.6059
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        ],
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        "role_id": 9,
        "score": 0.5491,
        "slug": "cloud-architect",
        "total_count": null
      },
      {
        "display_name": "DevOps Engineer",
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          {
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          {
            "kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
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        ],
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        "score": 0.5246,
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        "total_count": null
      },
      {
        "display_name": "Cyber Security Engineer",
        "kra_matches": [
          {
            "kra_text": "Defines secure engineering standards, secure coding guidelines, threat intelligence feeds, and compliance requirements for the organization.",
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            "similarity": 0.5205
          },
          {
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      }
    ],
    "skill_match_roles": [
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        "display_name": "PHP Backend Developer",
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        "matched_skills": [
          "Kafka",
          "MySQL",
          "PostgreSQL",
          "alerting",
          "logging"
        ],
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        "display_name": "Node.js Backend Developer",
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      },
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        "display_name": "Backend Developer",
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          "PostgreSQL",
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        "display_name": ".NET Backend Developer",
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        "matched_skills": [
          "Kafka",
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          "PostgreSQL",
          "alerting",
          "logging"
        ],
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      },
      {
        "display_name": "Python Backend Developer",
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        "matched_skills": [
          "Kafka",
          "MySQL",
          "PostgreSQL",
          "logging"
        ],
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  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "A",
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      "matched_count": null,
      "matched_skills": null,
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      "slug": "data-engineer",
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    },
    "confidence": 1.0,
    "is_new_role": false,
    "llm2_fired": false,
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    "matched_dimensions": [],
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    "matched_skills": [],
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    "centroid_updated": true,
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    "new_kra_attached": null,
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        "queue_id": 18550,
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        "role_slug": "data-engineer",
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        "role_slug": "data-engineer",
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        "role_slug": "data-engineer",
        "skill_name": "Cloud Native",
        "status": "pending"
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        "role_display_name": "Data Engineer",
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        "role_display_name": "Data Engineer",
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}
API 2 — extract-details
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "existing_alias_text": "MySQL",
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        "is_extractable": true,
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "existing_alias_text": "PostgreSQL",
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        "is_extractable": true,
        "skill_nature": "TOOL",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "existing_alias_text": "Snowflake",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "snowflake",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 379,
      "existing_alias_text": "Amazon S3",
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        "skill_nature": "CLOUD_SERVICE",
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
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      "matched_via": "embedding_alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2016,
      "existing_alias_text": "Artificial Intelligence",
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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      "matched_via": "alias"
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      "matched_via": "alias"
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          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Artificial Intelligence",
      "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": "Machine Learning",
          "alias_type": "CANONICAL",
          "id": 2015,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Machine Learning",
        "id": 1356,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "machine-learning",
        "sub_category_id": 1024,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "AI Governance and Model Security",
            "id": 50,
            "rationale": "Controls and documentation used to make models safer, auditable, and compliant. ML engineers use this to manage model risk, supply chain integrity, and governance requirements.",
            "slug": "ai-governance-and-model-security",
            "source": "db"
          },
          "input_skill": "Machine Learning",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AI Engineer",
              "id": 13,
              "rationale": null,
              "role_archetype": null,
              "slug": "ai-engineer",
              "source": "db"
            },
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "MLOps Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "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": "Machine Learning",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Machine Learning",
      "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": "SAP AI Core",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "sap-ai-core",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Predictive Analytics",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "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": "predictive-analytics",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Security",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concepts",
          "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": "security",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Monitoring",
          "alias_type": "CANONICAL",
          "id": 1854,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Monitoring",
        "id": 1218,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "monitoring",
        "sub_category_id": 924,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Incident Triage",
            "id": 155,
            "rationale": "Telemetry, alerting, and troubleshooting practices used to diagnose failed builds, broken deployments, and unhealthy release environments. This is a coherent cluster because delivery reliability depends on quickly identifying where the workflow failed.",
            "slug": "observability-and-incident-triage",
            "source": "db"
          },
          "input_skill": "Monitoring",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Monitoring",
      "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": "logging",
          "alias_type": "CANONICAL",
          "id": 3683,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Logging",
          "alias_type": "CANONICAL",
          "id": 2579,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "logging",
        "id": 1624,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "logging",
        "sub_category_id": 752,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Diagnostics",
            "id": 287,
            "rationale": "Instrumentation and troubleshooting practices used to understand Java service behavior in production and lower environments. This cluster covers logs, metrics, traces, correlation IDs, and root-cause analysis from service telemetry.",
            "slug": "observability-and-diagnostics",
            "source": "db"
          },
          "input_skill": "Logging",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Java Backend Developer",
              "id": 79,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "java-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": "Observability and Incident Response",
            "id": 10,
            "rationale": "Instrumentation and production troubleshooting practices used to keep backend services reliable. Includes logs, metrics, traces, alerting, dashboards, and incident diagnosis.",
            "slug": "observability-and-incident-response",
            "source": "db"
          },
          "input_skill": "Logging",
          "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": "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": "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"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Operations",
            "id": 143,
            "rationale": "Monitoring, logging, tracing, and operational readiness patterns used to keep cloud platforms supportable. Cloud Architects use this to define what telemetry and operational controls workloads must expose.",
            "slug": "observability-and-operations",
            "source": "db"
          },
          "input_skill": "Logging",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Logging",
      "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": "alerting",
          "alias_type": "CANONICAL",
          "id": 1444,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "alerting",
        "id": 882,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "alerting",
        "sub_category_id": 3472,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Backend Observability, Logging, and Diagnostics",
            "id": 388,
            "rationale": "Instrumentation and troubleshooting practices used to understand and improve backend service behavior in production and lower environments. This includes logs, metrics, traces, alerting, dashboards, structured logging, distributed tracing, health checks, and root-cause analysis using ecosystem-specific tools such as SLF4J, Logback, Micrometer, OpenTelemetry, Prometheus, Grafana, ILogger, Serilog, and Application Insights.",
            "slug": "backend-observability-logging-and-diagnostics",
            "source": "db"
          },
          "input_skill": "Alerting",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Incident Response",
            "id": 10,
            "rationale": "Instrumentation and production troubleshooting practices used to keep backend services reliable. Includes logs, metrics, traces, alerting, dashboards, and incident diagnosis.",
            "slug": "observability-and-incident-response",
            "source": "db"
          },
          "input_skill": "Alerting",
          "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": "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": "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"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Incident Triage",
            "id": 155,
            "rationale": "Telemetry, alerting, and troubleshooting practices used to diagnose failed builds, broken deployments, and unhealthy release environments. This is a coherent cluster because delivery reliability depends on quickly identifying where the workflow failed.",
            "slug": "observability-and-incident-triage",
            "source": "db"
          },
          "input_skill": "Alerting",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Alerting",
      "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": "Automation",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concepts",
          "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": "automation",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "SAP S/4HANA",
      "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": "sap-s-4hana",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "SAP Datasphere",
    "SAP Business Data Cloud",
    "SAP Analytics Cloud",
    "SLT Replication Server",
    "SAP HANA Cloud",
    "Oracle",
    "Cloud Native",
    "Data Integration",
    "Data Management",
    "Data Mesh",
    "Data Governance",
    "Data Science",
    "SAP AI Core",
    "Predictive Analytics",
    "Security",
    "Automation",
    "SAP S/4HANA"
  ]
}
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 php-backend-developer 0.19 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "SAP Datasphere",
      "tag": "new"
    },
    {
      "skill": "SAP Business Data Cloud",
      "tag": "new"
    },
    {
      "skill": "SAP Analytics Cloud",
      "tag": "new"
    },
    {
      "skill": "SLT Replication Server",
      "tag": "new"
    },
    {
      "skill": "SAP HANA Cloud",
      "tag": "new"
    },
    {
      "skill": "Oracle",
      "tag": "new"
    },
    {
      "skill": "Kafka",
      "tag": "in_db"
    },
    {
      "skill": "MySQL",
      "tag": "in_db"
    },
    {
      "skill": "PostgreSQL",
      "tag": "in_db"
    },
    {
      "skill": "Snowflake",
      "tag": "in_db"
    },
    {
      "skill": "Amazon S3",
      "tag": "in_db"
    },
    {
      "skill": "Cloud Native",
      "tag": "new"
    },
    {
      "skill": "Data Integration",
      "tag": "new"
    },
    {
      "skill": "Data Management",
      "tag": "new"
    },
    {
      "skill": "Data Mesh",
      "tag": "new"
    },
    {
      "skill": "Data Modeling",
      "tag": "in_db"
    },
    {
      "skill": "Data Governance",
      "tag": "new"
    },
    {
      "skill": "Data Science",
      "tag": "new"
    },
    {
      "skill": "Artificial Intelligence",
      "tag": "in_db"
    },
    {
      "skill": "Machine Learning",
      "tag": "in_db"
    },
    {
      "skill": "SAP AI Core",
      "tag": "new"
    },
    {
      "skill": "Predictive Analytics",
      "tag": "new"
    },
    {
      "skill": "Security",
      "tag": "new"
    },
    {
      "skill": "Monitoring",
      "tag": "in_db"
    },
    {
      "skill": "Logging",
      "tag": "in_db"
    },
    {
      "skill": "Alerting",
      "tag": "in_db"
    },
    {
      "skill": "Automation",
      "tag": "new"
    },
    {
      "skill": "SAP S/4HANA",
      "tag": "new"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Asynchronous Messaging and Event Streaming",
          "id": 297,
          "rationale": "Asynchronous communication patterns and broker technologies used to decouple backend services and move work off the request path. Includes queues, pub/sub, event streams, consumer groups, dead-letter queues, and delivery semantics across systems such as Kafka, RabbitMQ, NATS, SQS/SNS, Pulsar, and ActiveMQ.",
          "slug": "asynchronous-messaging-and-event-streaming",
          "source": "db"
        },
        "dimension_id": 297,
        "input_skill": "Kafka",
        "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": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Go Backend Developer",
            "id": 81,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "go-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": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 36,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
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