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Pipeline run

5c88d2ac-228b-4958-b101-a3008380b54c

Pipeline LLM cost (USD)
API 1: $0.0123 API 2: $0.0003 API 3: $0.0000 Total: $0.0126

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · Cloud/SaaS Platform Engineering
Build and operate IBM’s Lakehouse SaaS across multiple cloud providers, using Kubernetes/OpenShift and CI/CD to automate deployment, monitoring, logging, and alerting. Also architect and implement new features, collaborating in Agile teams with strong cloud/security and data-platform knowledge.
""Work in an Agile, collaborative environment to build, deploy, configure, and maintain Lakehouse (SaaS) on multiple hyperscalers.""
Tech stack maturity
Modern Cloud Native
The role centers on multi-cloud and hybrid-cloud engineering with Kubernetes, CI/CD, and major cloud platforms, which aligns best with a modern cloud-native stack.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.20 / 5
· Title match
Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3): AI, Artificial Intelligence
Evidence — skills matched in JD (23)
Agile Jenkins Artifactory Kubernetes OpenShift Amazon Web Services IBM Cloud Microsoft Azure Google Cloud Platform SaaS Cloud Security Go Python Ruby Git GitHub RDBMS Hive Metastore Iceberg Delta Lake Hudi Presto Spark
Skill cluster (6 dimension groups, role-scoped)
Cloud Platforms
IBM Cloud Microsoft Azure
Cloud Provider Platforms
Google Cloud Platform
Container Orchestration Platforms
Kubernetes
Go Language and Toolchain
Go
Python Programming
Python
Cross-cutting / unaligned
Agile Jenkins Artifactory OpenShift Amazon Web Services SaaS Cloud Security Ruby Git GitHub RDBMS Hive Metastore Iceberg Delta Lake Hudi Presto Spark
Show KRA description ↓
Software development is critical to the success of IBM and our clients worldwide. At IBM, you will use the latest software development tools, techniques and approaches and work with leading minds in the industry to build solutions you can be proud of. This opportunity is for a new Open Data Lakehouse product that IBM intends to bring to the market . We are looking for seasoned professionals who are passionate about data, familiar with data warehouse, data lake and data lakehouse products in the market. • Work in an Agile, collaborative environment to build, deploy, configure, and maintain Lakehouse (SaaS) on multiple hyperscalers. • You will work in an innovation driven, collaborative environment to understand requirements, architect, design and implement functionalities / features. • Use continuous integration tools like Jenkins and Artifactory to build automation pipeline to deploy different service workloads for Lakehouse. • Collaborate multiple development teams to enable a continuous integration environment that sustains high productivity levels and emphasizes defect prevention techniques. • Design and implement automation for deployment, monitoring, logging, alerting of large-scale Lakehouse environments. • 6+ Years of strong development experience in cloud technologies. • Working knowledge on Virtualization, containerization technologies, containers orchestration software (Kubernetes and OpenShift) and cloud platforms • Working knowledge on Kubernetes clusters administration • Working experience on cloud services (Amazon Web Services, IBM Cloud, Microsoft Azure, Google Cloud Platform) • Working knowledge on Development and operation of fully managed SaaS services • Working knowledge on Cloud SaaS security • Languages: GO, Python, Ruby • CI/CD Tools: Jenkins, Artifactory • Working knowledge on RDBMS, data warehouse, data lakes • Source Control Tools: Git, GitHub • Excellent verbal and written communication skills with the ability to present complex technical information. • Self-starter, organized, willing to learn and solve things on your own. • Growth-mindset: Willingness to learn new technologies and processes. • Familiarity with Hive metastore and open data formats (Iceberg, Delta Lake, Hudi) • Open-source data engines: Presto, Spark • Data governance management • Open-source software development

Signals

Skill ml-engineer
0.41
Alias
KRA devops-engineer
0.62

Post-classification

Centroidupdated · n=3
Alias collision log
New-role queue
New skills captured8
New KRA capturedyes

Captured for admin review

Artifactory primary Multi-Cloud / Hybrid Cloud Engineer pending
OpenShift primary Multi-Cloud / Hybrid Cloud Engineer pending
SaaS primary Multi-Cloud / Hybrid Cloud Engineer pending
Cloud Security primary Multi-Cloud / Hybrid Cloud Engineer pending
Hive Metastore Multi-Cloud / Hybrid Cloud Engineer pending
Iceberg Multi-Cloud / Hybrid Cloud Engineer pending
Hudi Multi-Cloud / Hybrid Cloud Engineer pending
Presto Multi-Cloud / Hybrid Cloud Engineer pending
R&R fragment (sim 0.00) Multi-Cloud / Hybrid Cloud Engineer pending

Software development is critical to the success of IBM and our clients worldwide. At IBM, you will use the latest software development tools, techniques and approaches and work with leading minds in t…

Status: completed Created: 2026-05-27T15:40:12.138563Z Updated: 2026-06-12T16:08:06.323591Z API 3 duration: 53609 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

Multi-Cloud / Hybrid Cloud Engineer

Hybrid Cloud Developer

sub-role · 0.95 domain · Cloud Engineering CASE DOMAIN

slug: multi-cloud-hybrid-cloud-engineer · id: 39 · source: db · sub-role slug: hybrid-cloud-developer

Domain=Cloud Engineering → sub-role hybrid-cloud-developer; The JD centers on building and operating a Lakehouse SaaS across multiple hyperscalers with Kubernetes, CI/CD, and cloud platform experience, which best fits a multi-cloud/hybrid cloud engineer.

Matched skills

JenkinsArtifactoryKubernetesOpenShiftAmazon Web ServicesIBM CloudMicrosoft AzureGoogle Cloud PlatformGOPythonRubyGitGitHubHive metastoreIcebergDelta LakeHudiPrestoSpark

Matched dimensions

Multi-cloud SaaS engineeringLakehouse platform operationsCI/CD automationKubernetes cluster administrationCloud-native deployment and observabilityData platform engineeringData governance

Matched KRAs

build, deploy, configure, and maintain Lakehouse (SaaS)architect, design and implement functionalities / featuresuse continuous integration tools like Jenkins and Artifactoryenable a continuous integration environmentdesign and implement automation for deployment, monitoring, logging, alertingwork on cloud servicesworking knowledge on Kubernetes clusters administration

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
0
Skipped

Job description

649204BR

Introduction

At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. Are you ready to lead in this new era of technology and solve some of the world's most challenging problems? If so, lets talk.

Your Role and Responsibilities

Software development is critical to the success of IBM and our clients worldwide. At IBM, you will use the latest software development tools, techniques and approaches and work with leading minds in the industry to build solutions you can be proud of.

This opportunity is for a new Open Data Lakehouse product that IBM intends to bring to the market . We are looking for seasoned professionals who are passionate about data, familiar with data warehouse, data lake and data lakehouse products in the market.

• Work in an Agile, collaborative environment to build, deploy, configure, and maintain Lakehouse (SaaS) on multiple hyperscalers.
• You will work in an innovation driven, collaborative environment to understand requirements, architect, design and implement functionalities / features.
• Use continuous integration tools like Jenkins and Artifactory to build automation pipeline to deploy different service workloads for Lakehouse.
• Collaborate multiple development teams to enable a continuous integration environment that sustains high productivity levels and emphasizes defect prevention techniques.
• Design and implement automation for deployment, monitoring, logging, alerting of large-scale Lakehouse environments.


Required Technical and Professional Expertise

• 6+ Years of strong development experience in cloud technologies.
• Working knowledge on Virtualization, containerization technologies, containers orchestration software (Kubernetes and OpenShift) and cloud platforms
• Working knowledge on Kubernetes clusters administration
• Working experience on cloud services (Amazon Web Services, IBM Cloud, Microsoft Azure, Google Cloud Platform)
• Working knowledge on Development and operation of fully managed SaaS services
• Working knowledge on Cloud SaaS security
• Languages: GO, Python, Ruby
• CI/CD Tools: Jenkins, Artifactory
• Working knowledge on RDBMS, data warehouse, data lakes 
• Source Control Tools: Git, GitHub
• Excellent verbal and written communication skills with the ability to present complex technical information.
• Self-starter, organized, willing to learn and solve things on your own.
• Growth-mindset: Willingness to learn new technologies and processes.


Preferred Technical And Professional Expertise

• Familiarity with Hive metastore and open data formats (Iceberg, Delta Lake, Hudi)
• Open-source data engines: Presto, Spark
• Data governance management
• Open-source software development


About Business Unit

IBM’s Cloud and Cognitive software business is committed to bringing the power of IBM’s Cloud and Watson/AI technologies to life for our clients and ecosystem partners around the world. IBM provides you with the most comprehensive and consistent approach to development, security and operations across hybrid environments—with complete software solutions for business and IT operations, development, data science, security, and management. Our experts and software capabilities help organizations develop applications once and deploy them anywhere, integrate security across the breadth of their IT estate, and automate operations with management visibility. With IBM, you also have access to new skills and methods, governance and management approaches, and a deep ecosystem of industry experts and partners.

This job requires you to be fully COVID-19 vaccinated prior to your start date and proof of vaccination status will be required before your start date. During the Onboarding process you will be asked to confirm your vaccination status, in case you are unable to get vaccinated for any reason, you can let us know at that stage. Please let us know if you are unable to be vaccinated due to medical or religious reasons. IBM will consider such requests on a case by case basis subject to submission of required proof by the candidate before a stipulated date.

Your Life @ IBM

In a world where technology never stands still, we understand that, dedication to our clients success, innovation that matters, and trust and personal responsibility in all our relationships, lives in what we do as IBMers as we strive to be the catalyst that makes the world work better.

Being an IBMer means you’ll be able to learn and develop yourself and your career, you’ll be encouraged to be courageous and experiment everyday, all whilst having continuous trust and support in an environment where everyone can thrive whatever their personal or professional background.

Our IBMers are growth minded, always staying curious, open to feedback and learning new information and skills to constantly transform themselves and our company. They are trusted to provide on-going feedback to help other IBMers grow, as well as collaborate with colleagues keeping in mind a team focused approach to include different perspectives to drive exceptional outcomes for our customers. The courage our IBMers have to make critical decisions everyday is essential to IBM becoming the catalyst for progress, always embracing challenges with resources they have to hand, a can-do attitude and always striving for an outcome focused approach within everything that they do.

Are you ready to be an IBMer?

About IBM

IBM’s greatest invention is the IBMer. We believe that through the application of intelligence, reason and science, we can improve business, society and the human condition, bringing the power of an open hybrid cloud and AI strategy to life for our clients and partners around the world.Restlessly reinventing since 1911, we are not only one of the largest corporate organizations in the world, we’re also one of the biggest technology and consulting employers, with many of the Fortune 50 companies relying on the IBM Cloud to run their business. At IBM, we pride ourselves on being an early adopter of artificial intelligence, quantum computing and blockchain. Now it’s time for you to join us on our journey to being a responsible technology innovator and a force for good in the world.

Location Statement

When applying to jobs of your interest, we recommend that you do so for those that match your experience and expertise. Our recruiters advise that you apply to not more than 3 roles in a year for the best candidate experience.

For additional information about location requirements, please discuss with the recruiter following submission of your application.

Being You @ IBM

IBM is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, pregnancy, disability, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

Skills from this JD

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

Agile Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Agile id=520 · agile

Aliases — catalog

  • Agile (CANONICAL) primary

Context tags (catalog)

Kanban SAFe Scrum backlog backlog grooming burndown burndown chart continuous delivery continuous improvement cross-functional daily standup epics incremental development iteration iteration planning lean product backlog product owner retrospective sprint sprint planning stand-up story points user stories velocity

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Agile
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Agile appears in a large share of software job descriptions and is a standard hiring-pipeline requirement; Scrum/Kanban are commonly listed alongside it, showing broad market adoption.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
3594
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Software Concepts, Patterns & Practices Catalog dimension db id 478

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Software Concepts, Patterns & Practices
software-concepts-patterns-practices
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Jenkins Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Jenkins id=283 · jenkins

Aliases — catalog

  • Jenkins (CANONICAL) primary

Context tags (catalog)

Blue Ocean CI/CD Declarative Pipeline Docker Groovy Jenkinsfile Kubernetes agents artifact repository artifacts automation build triggers integration multibranch pipeline pipeline plugins shared libraries stages test automation version control webhooks

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Ci Cd Tool
Vendor
CloudBees
License
mit
Year introduced
2011
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Jenkins remains a common CI/CD requirement in job postings and enterprise DevOps stacks, with broad plugin ecosystem and long-running GitHub activity despite newer alternatives.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
184
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • CI/CD Pipeline Platforms Catalog dimension db id 150

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • CI/CD for Machine Learning Catalog dimension db id 56

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Artifactory 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
DevOps Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Kubernetes Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Kubernetes id=726 · kubernetes

Aliases — catalog

  • Kubernetes (CANONICAL) primary
  • Kubernetes 1.0+ (VERSION)
  • Kubernetes 1.x (VERSION)
  • Kubernetes v1 (VERSION)
  • k8s (VERSION)
  • kubernetes 1.x (VERSION)
  • kubernetes latest (VERSION)

Context tags (catalog)

CI/CD Cluster Autoscaler ConfigMap DaemonSet Deployment Docker Grafana Helm Ingress Istio K8s Kubelet Namespace Pod Prometheus RBAC Secret Service StatefulSet containerization deployment etcd kubectl load balancing microservices namespace orchestration persistent storage scalability service mesh

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Container Orchestration Platform
Vendor
Cloud Native Computing Foundation
License
apache_2
Year introduced
2014
Confidence
0.90
Version strategy
SEPARATE_ENTITY
Version tag
1.30

Maturity reasoning: Broadly adopted in cloud-native stacks; Kubernetes appears in a large share of DevOps/SRE job descriptions and is the default orchestration platform across major cloud vendors.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
557
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Container Orchestration Platforms Catalog dimension db id 134

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, DevOps Engineer

  • Kubernetes for ML Workloads Catalog dimension db id 47

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Container Orchestration Platforms
container-orchestration-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubernetes for ML Workloads
kubernetes-for-ml-workloads
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
OpenShift 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
Amazon Web Services Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Amazon Web Services id=3881 · amazon-web-services

Aliases — catalog

  • Amazon Web Services (CANONICAL) primary

Context tags (catalog)

API Gateway AWS CLI CloudFormation CloudFront CloudWatch DynamoDB EC2 EKS Elastic Beanstalk IAM Lambda RDS S3 Serverless VPC

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Amazon
License
other_open
Year introduced
2006
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: AWS is a hiring-pipeline staple: it appears in a large share of cloud/DevOps job descriptions and remains a top cloud provider in market-share reports, with broad certification and ecosystem demand.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
46
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud & Hosting Providers Catalog dimension db id 414

    Library dimension (catalog)

    Roles linked in library: PHP Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud & Hosting Providers
cloud-hosting-providers
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
IBM Cloud Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: IBM Cloud id=426 · ibm-cloud

Aliases — catalog

  • IBM Cloud (CANONICAL) primary

Context tags (catalog)

API Connect API Gateway CI/CD CLI Cloud Foundry Cloud Functions Cloud Object Storage Cloud Pak DevOps Helm IAM IBM Cloud CLI IBM Cloud Databases IBM Cloud Functions IBM Cloud Kubernetes Service IBM Cloud Object Storage IBM Cloud Pak IBM Cloud Security IBM Watson Kubernetes Microservices OpenShift Red Hat OpenShift Serverless Terraform VPC Watson

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
IBM
License
other_open
Year introduced
2017
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: IBM Cloud appears in far fewer job postings than AWS/Azure/GCP and is usually requested only in IBM-centric enterprises; market demand is comparatively low-volume and specialized.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
46
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer

  • Cloud Platforms for AI Deployment Catalog dimension db id 211

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer, Cyber Security Engineer

  • Vendor Product Families Catalog dimension db id 477

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Microsoft Azure Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Microsoft Azure id=97 · microsoft-azure

Aliases — catalog

  • Microsoft Azure (CANONICAL) primary

Context tags (catalog)

AKS ARM templates App Service Azure Active Directory Azure App Service Azure Blob Storage Azure Cosmos DB Azure DevOps Azure Functions Azure Kubernetes Service Azure Logic Apps Azure Monitor Azure Resource Manager Azure SQL Azure Virtual Machines Bicep Cloud Services Entra ID Functions IaaS Infrastructure as Code Key Vault Logic Apps PaaS Resource Group Serverless Computing Service Bus Storage Account Virtual Machines cloud migration microservices serverless

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Microsoft
License
other_open
Year introduced
2010
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Azure appears in large volumes of cloud/DevOps job descriptions and is a core hyperscaler alongside AWS/GCP; Microsoft’s continued product investment and broad enterprise adoption signal mainstream demand.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
46
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud & Hosting Providers Catalog dimension db id 414

    Library dimension (catalog)

    Roles linked in library: PHP Backend Developer

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud & Hosting Providers
cloud-hosting-providers
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Platform Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Google Cloud Platform id=425 · google-cloud-platform

Aliases — catalog

  • Google Cloud Platform (CANONICAL) primary

Context tags (catalog)

Anthos App Engine Artifact Registry BigQuery Cloud Build Cloud Functions Cloud Monitoring Cloud Pub/Sub Cloud Run Cloud SQL Cloud Spanner Cloud Storage Compute Engine Dataflow Dataproc GCP GKE IAM Kubernetes Kubernetes Engine Pub/Sub Serverless Stackdriver Terraform VPC

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Google
License
other_open
Year introduced
2008
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: GCP appears in many cloud-engineering job descriptions alongside AWS/Azure, and Google continues expanding managed services and certifications, indicating broad hiring demand rather than niche use.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
46
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud & Hosting Providers Catalog dimension db id 414

    Library dimension (catalog)

    Roles linked in library: PHP Backend Developer

  • Cloud Provider Platforms Catalog dimension db id 131

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, Cloud Security Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer, Cyber Security Engineer

  • Vendor Product Families Catalog dimension db id 477

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud & Hosting Providers
cloud-hosting-providers
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SaaS 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
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Cloud 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
Security Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Go Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Go id=3 · go

Aliases — catalog

  • Go (CANONICAL) primary
  • Go 1.0 (VERSION)
  • Go 1.1 (VERSION)
  • Go 1.10 (VERSION)
  • Go 1.11 (VERSION)
  • Go 1.12 (VERSION)
  • Go 1.13 (VERSION)
  • Go 1.14 (VERSION)
  • Go 1.15 (VERSION)
  • Go 1.16 (VERSION)
  • Go 1.17 (VERSION)
  • Go 1.18 (VERSION)
  • Go 1.19 (VERSION)
  • Go 1.2 (VERSION)
  • Go 1.20 (VERSION)
  • Go 1.21 (VERSION)
  • Go 1.22 (VERSION)
  • Go 1.3 (VERSION)
  • Go 1.4 (VERSION)
  • Go 1.5 (VERSION)
  • Go 1.6 (VERSION)
  • Go 1.7 (VERSION)
  • Go 1.8 (VERSION)
  • Go 1.9 (VERSION)
  • Go 1.x (VERSION)
  • Golang (VERSION)
  • go 1.18 (VERSION)
  • go 1.19 (VERSION)
  • go 1.20 (VERSION)
  • go 1.21 (VERSION)
  • go 1.22 (VERSION)
  • go latest (VERSION)
  • go1.18 (VERSION)
  • go1.19 (VERSION)
  • go1.20 (VERSION)
  • go1.21 (VERSION)
  • go1.22 (VERSION)
  • go1.x (VERSION)
  • golang (VERSION)

Context tags (catalog)

Beego Channel Concurrency Context Docker Echo Gin Go modules Golang Goroutine HTTP HTTP server Interface JSON JSON encoding Kubernetes Microservices REST API Struct Testing channels concurrency context package context.Context error handling gRPC goroutines interfaces microservices middleware modules net/http protobuf structs testing

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
Google
License
bsd
Year introduced
2009
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Go appears in many current job descriptions for backend, cloud, and DevOps roles, and is a standard language in Kubernetes, Docker, and cloud-native stacks; strong GitHub and ecosystem adoption signal broad market demand.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Scripting & DSL Languages Catalog dimension db id 248

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Go Language and Toolchain Catalog dimension db id 367

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

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

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Go Language and Toolchain
go-language-and-toolchain
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

Aliases — catalog

  • Python (CANONICAL) primary
  • Python 2 (VERSION)
  • Python 2.x (VERSION)
  • Python 3 (VERSION)
  • Python 3.10 (VERSION)
  • Python 3.11 (VERSION)
  • Python 3.12 (VERSION)
  • Python 3.x (VERSION)
  • py (VERSION)
  • py2 (VERSION)
  • py3 (VERSION)
  • python 3 (VERSION)
  • python 3.x (VERSION)
  • python2 (VERSION)
  • python3 (VERSION)
  • python3.x (VERSION)

Context tags (catalog)

API Django FastAPI Flask Jupyter NumPy PEP 8 Pandas REST SQLAlchemy asyncio pandas pip pytest type hints venv virtualenv

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
PSF
License
mit
Year introduced
1991
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3

Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Scripting & DSL Languages Catalog dimension db id 248

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

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

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • Python Programming Catalog dimension db id 290

    Library dimension (catalog)

    Roles linked in library: Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Ruby Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Ruby id=300 · ruby

Aliases — catalog

  • Ruby (CANONICAL) primary
  • CRuby (VERSION)
  • MRI (VERSION)
  • Ruby 2 (VERSION)
  • Ruby 2.6 (VERSION)
  • Ruby 2.7 (VERSION)
  • Ruby 2.x (VERSION)
  • Ruby 3 (VERSION)
  • Ruby 3.2 (VERSION)
  • Ruby 3.3 (VERSION)
  • Ruby 3.x (VERSION)
  • ruby 3 (VERSION)
  • ruby 3.3 (VERSION)
  • ruby 3.x (VERSION)
  • ruby3 (VERSION)
  • ruby3.3 (VERSION)
  • ruby3.x (VERSION)

Context tags (catalog)

API ActiveJob ActiveRecord Bundler Capistrano Capybara ERB Gem Gemfile JRuby MVC Minitest Puma RSpec Rails Rake Rubocop Sidekiq Sinatra YAML

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
Ruby Association
License
mit
Year introduced
1995
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3.3

Maturity reasoning: Ruby remains common in job postings, especially for Rails backends and legacy web stacks; it’s still a standard hiring-pipeline language rather than a niche or sunset tech.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • Ruby Language and Syntax Catalog dimension db id 415

    Library dimension (catalog)

    Roles linked in library: Ruby Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Ruby Language and Syntax
ruby-language-and-syntax
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Git Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Git id=1002 · git

Aliases — catalog

  • Git (CANONICAL)

Context tags (catalog)

CI/CD GitHub GitLab branching checkout clone commit fork merging pull request rebase remote repository stash versioning

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Version Control Tool
Vendor
Linus Torvalds
License
gpl_v2
Year introduced
2005
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Git is a hiring-pipeline staple: it appears in the vast majority of software engineering job descriptions and is the default VCS on GitHub/GitLab/Bitbucket.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
730
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)
GitHub Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: GitHub id=280 · github

Aliases — catalog

  • GitHub (CANONICAL) primary

Context tags (catalog)

CI/CD Git GitHub Actions GitHub Enterprise GitHub Pages SSH keys actions branch protection branches code review collaboration commit history fork forking issue tracker issues markdown merge conflicts merge request pull request pull requests release tags repositories repository version control webhooks

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Devops Platform
Vendor
GitHub, Inc.
License
other_open
Year introduced
2008
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: GitHub appears in a very high volume of engineering JDs for source control, code review, and CI/CD; it’s a standard hiring-pipeline skill across teams.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
170
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • CI/CD Pipeline Platforms Catalog dimension db id 150

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • CI/CD for Machine Learning Catalog dimension db id 56

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
RDBMS Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: RDBMS id=1355 · rdbms

Aliases — catalog

  • RDBMS (CANONICAL)

Context tags (catalog)

ACID ER diagrams SQL backup and recovery data integrity data modeling database tuning foreign keys indexes joins normalization query optimization schema design stored procedures transactions views

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database Management System
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: RDBMS is a core requirement in many job descriptions across backend, data, and DBA roles; PostgreSQL, MySQL, and SQL Server remain standard enterprise stacks.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
1023
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)
Hive Metastore 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
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Iceberg 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
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Delta Lake Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Delta Lake id=237 · delta-lake

Aliases — catalog

  • Delta Lake (CANONICAL) primary

Context tags (catalog)

ACID transactions Apache Spark CDC Databricks Delta Engine ETL Lakehouse architecture MERGE INTO OPTIMIZE Parquet Unity Catalog Z-Order batch processing cloud storage data governance data lake data lakehouse data pipeline data reliability partition pruning schema enforcement schema evolution streaming data streaming ingestion time travel

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Table Format Tool
Vendor
Databricks
License
apache_2
Year introduced
2017
Confidence
0.72
Version strategy
NOT_APPLICABLE

Maturity reasoning: Delta Lake appears frequently in data engineering JDs and cloud vendor docs, especially alongside Databricks/Spark for lakehouse stacks; it’s a common hiring-pipeline skill rather than a niche tool.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
1170
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Model and Data Versioning Catalog dimension db id 48

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Model and Data Versioning
model-and-data-versioning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Hudi 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
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Presto 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
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Spark Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Spark id=1350 · apache-spark

Aliases — catalog

  • Apache Spark (CANONICAL)
  • apache spark 3 (VERSION)
  • spark (VERSION)
  • spark 3 (VERSION)
  • spark 3.x (VERSION)
  • spark3 (VERSION)

Context tags (catalog)

Apache Kafka Cluster Manager DAGScheduler Data Lake DataFrame ETL Hadoop MLlib Machine Learning PySpark RDD Scala Spark SQL Spark Streaming SparkSession

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Distributed Data Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2010
Confidence
0.94
Version strategy
SEPARATE_ENTITY
Version tag
3.x

Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
1021
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

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
Agile in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Agile in_db
Software Concepts, Patterns & Practices
software-concepts-patterns-practices
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Jenkins in_db
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Jenkins in_db
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubernetes in_db
Container Orchestration Platforms
container-orchestration-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubernetes in_db
Kubernetes for ML Workloads
kubernetes-for-ml-workloads
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Amazon Web Services in_db
Cloud & Hosting Providers
cloud-hosting-providers
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
IBM Cloud in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
IBM Cloud in_db
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
IBM Cloud in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
IBM Cloud in_db
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Microsoft Azure in_db
Cloud & Hosting Providers
cloud-hosting-providers
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Microsoft Azure in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Platform in_db
Cloud & Hosting Providers
cloud-hosting-providers
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Platform in_db
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Platform in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Platform in_db
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Go in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Go in_db
Go Language and Toolchain
go-language-and-toolchain
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Go in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Go in_db
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Go in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Ruby in_db
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Ruby in_db
Ruby Language and Syntax
ruby-language-and-syntax
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Git in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GitHub in_db
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GitHub in_db
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
RDBMS in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Delta Lake in_db
Model and Data Versioning
model-and-data-versioning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Artifactory | type=DevOps Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed OpenShift | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed SaaS | type=Cloud Platforms subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed Cloud Security | type=Security Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Hive Metastore | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Iceberg | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Hudi | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Presto | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
nano JD Parser — gpt-4.1-nano click to toggle
RoleSoftware Developer
CompanyIBM
Experience6+ Years of strong development experience in cloud technologies.
DomainIT Services & Consulting
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "IBM\u2019s greatest invention is the",
      "last_5_words": "and a force for good in the world."
    },
    "text": "IBM\u2019s greatest invention is the IBMer. We believe that through the application of intelligence, reason and science, we can improve business, society and the human condition, bringing the power of an open hybrid cloud and AI strategy to life for our clients and partners around the world.Restlessly reinventing since 1911, we are not only one of the largest corporate organizations in the world, we\u2019re also one of the biggest technology and consulting employers, with many of the Fortune 50 companies relying on the IBM Cloud to run their business. At IBM, we pride ourselves on being an early adopter of artificial intelligence, quantum computing and blockchain. Now it\u2019s time for you to join us on our journey to being a responsible technology innovator and a force for good in the world.",
    "word_count": 84
  },
  "certifications": [],
  "company_name": "IBM",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "Cloud Services",
        "Technology Consulting"
      ],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": 6,
    "raw": "6+ Years of strong development experience in cloud technologies."
  },
  "job_locations": [],
  "role": "Software Developer",
  "role_aliases": [
    "Software Engineer",
    "SWE",
    "Cloud Developer"
  ],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 5,
      "heading": "Your Role and Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Software development is critical to",
        "last_5_words": "monitoring, logging, alerting of large-scale"
      },
      "text": "Software development is critical to the success of IBM and our clients worldwide. At IBM, you will use the latest software development tools, techniques and approaches and work with leading minds in the industry to build solutions you can be proud of.\n\nThis opportunity is for a new Open Data Lakehouse product that IBM intends to bring to the market . We are looking for seasoned professionals who are passionate about data, familiar with data warehouse, data lake and data lakehouse products in the market.\n\n\u2022 Work in an Agile, collaborative environment to build, deploy, configure, and maintain Lakehouse (SaaS) on multiple hyperscalers.\n\u2022 You will work in an innovation driven, collaborative environment to understand requirements, architect, design and implement functionalities / features.\n\u2022 Use continuous integration tools like Jenkins and Artifactory to build automation pipeline to deploy different service workloads for Lakehouse.\n\u2022 Collaborate multiple development teams to enable a continuous integration environment that sustains high productivity levels and emphasizes defect prevention techniques.\n\u2022 Design and implement automation for deployment, monitoring, logging, alerting of large-scale Lakehouse environments.",
      "word_count": 263
    },
    {
      "bullet_count": 12,
      "heading": "Required Technical and Professional Expertise",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 6+ Years of strong development",
        "last_5_words": "and processes."
      },
      "text": "\u2022 6+ Years of strong development experience in cloud technologies.\n\u2022 Working knowledge on Virtualization, containerization technologies, containers orchestration software (Kubernetes and OpenShift) and cloud platforms\n\u2022 Working knowledge on Kubernetes clusters administration\n\u2022 Working experience on cloud services (Amazon Web Services, IBM Cloud, Microsoft Azure, Google Cloud Platform)\n\u2022 Working knowledge on Development and operation of fully managed SaaS services\n\u2022 Working knowledge on Cloud SaaS security\n\u2022 Languages: GO, Python, Ruby\n\u2022 CI/CD Tools: Jenkins, Artifactory\n\u2022 Working knowledge on RDBMS, data warehouse, data lakes \n\u2022 Source Control Tools: Git, GitHub\n\u2022 Excellent verbal and written communication skills with the ability to present complex technical information.\n\u2022 Self-starter, organized, willing to learn and solve things on your own.\n\u2022 Growth-mindset: Willingness to learn new technologies and processes.",
      "word_count": 155
    },
    {
      "bullet_count": 4,
      "heading": "Preferred Technical And Professional Expertise",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Familiarity with Hive metastore",
        "last_5_words": "governance management"
      },
      "text": "\u2022 Familiarity with Hive metastore and open data formats (Iceberg, Delta Lake, Hudi)\n\u2022 Open-source data engines: Presto, Spark\n\u2022 Data governance management\n\u2022 Open-source software development",
      "word_count": 32
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Agile"
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API 2 — extract-details
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            "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
            "slug": "programming-languages-for-data-work",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages for ML Systems",
            "id": 39,
            "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
            "slug": "programming-languages-for-ml-systems",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_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": "Programming Languages for XR",
            "id": 97,
            "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
            "slug": "programming-languages-for-xr",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AR/VR Engineer",
              "id": 8,
              "rationale": null,
              "role_archetype": null,
              "slug": "ar-vr-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Python Programming",
            "id": 290,
            "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
            "slug": "python-programming",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Python",
      "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": "Ruby",
          "alias_type": "CANONICAL",
          "id": 606,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "CRuby",
          "alias_type": "VERSION",
          "id": 608,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "MRI",
          "alias_type": "VERSION",
          "id": 607,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Ruby 2",
          "alias_type": "VERSION",
          "id": 609,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Ruby 2.6",
          "alias_type": "VERSION",
          "id": 611,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Ruby 2.7",
          "alias_type": "VERSION",
          "id": 612,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Ruby 2.x",
          "alias_type": "VERSION",
          "id": 613,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Ruby 3",
          "alias_type": "VERSION",
          "id": 610,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Ruby 3.2",
          "alias_type": "VERSION",
          "id": 614,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Ruby 3.3",
          "alias_type": "VERSION",
          "id": 615,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Ruby 3.x",
          "alias_type": "VERSION",
          "id": 616,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "ruby 3",
          "alias_type": "VERSION",
          "id": 5649,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "ruby 3.3",
          "alias_type": "VERSION",
          "id": 5652,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "ruby 3.x",
          "alias_type": "VERSION",
          "id": 5653,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "ruby3",
          "alias_type": "VERSION",
          "id": 5648,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "ruby3.3",
          "alias_type": "VERSION",
          "id": 5650,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "ruby3.x",
          "alias_type": "VERSION",
          "id": 5651,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "Ruby",
        "id": 300,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "ruby",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages and Scripting",
            "id": 59,
            "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
            "slug": "programming-languages-and-scripting",
            "source": "db"
          },
          "input_skill": "Ruby",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cyber Security Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Ruby Language and Syntax",
            "id": 415,
            "rationale": "Core Ruby implementation skills used to build backend business logic, helpers, and service internals. This is the primary coding surface for the role and includes idiomatic Ruby used in production codebases.",
            "slug": "ruby-language-and-syntax",
            "source": "db"
          },
          "input_skill": "Ruby",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Ruby",
      "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": "Git",
          "alias_type": "CANONICAL",
          "id": 1613,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 13,
        "display_name": "Git",
        "id": 1002,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "git",
        "sub_category_id": 730,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Git",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Git",
      "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": "GitHub",
          "alias_type": "CANONICAL",
          "id": 541,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "GitHub",
        "id": 280,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "github",
        "sub_category_id": 170,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "CI/CD Pipeline Platforms",
            "id": 150,
            "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
            "slug": "ci-cd-pipeline-platforms",
            "source": "db"
          },
          "input_skill": "GitHub",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "CI/CD for Machine Learning",
            "id": 56,
            "rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
            "slug": "ci-cd-for-machine-learning",
            "source": "db"
          },
          "input_skill": "GitHub",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "GitHub",
      "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": "RDBMS",
          "alias_type": "CANONICAL",
          "id": 2014,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 3,
        "display_name": "RDBMS",
        "id": 1355,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "rdbms",
        "sub_category_id": 1023,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "RDBMS",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "RDBMS",
      "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": "Hive Metastore",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "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": "hive-metastore",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Iceberg",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "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": "iceberg",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Delta Lake",
          "alias_type": "CANONICAL",
          "id": 498,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 13,
        "display_name": "Delta Lake",
        "id": 237,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "delta-lake",
        "sub_category_id": 1170,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Model and Data Versioning",
            "id": 48,
            "rationale": "Versioning systems for datasets, features, and model artifacts at the storage layer. This enables reproducible training, rollback, lineage of artifacts, and controlled promotion of model assets.",
            "slug": "model-and-data-versioning",
            "source": "db"
          },
          "input_skill": "Delta Lake",
          "llm_role": null,
          "roles_from_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"
            }
          ]
        }
      ],
      "input_skill": "Delta Lake",
      "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": "Hudi",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "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": "hudi",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Presto",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "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": "presto",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Apache Spark",
          "alias_type": "CANONICAL",
          "id": 2004,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "apache spark 3",
          "alias_type": "VERSION",
          "id": 2006,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "spark",
          "alias_type": "VERSION",
          "id": 2510,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "spark 3",
          "alias_type": "VERSION",
          "id": 2007,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "spark 3.x",
          "alias_type": "VERSION",
          "id": 2009,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "spark3",
          "alias_type": "VERSION",
          "id": 2008,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 5,
        "display_name": "Apache Spark",
        "id": 1350,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-spark",
        "sub_category_id": 1021,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "ETL and ELT Tooling",
            "id": 24,
            "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
            "slug": "etl-and-elt-tooling",
            "source": "db"
          },
          "input_skill": "Spark",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Spark",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Artifactory",
    "OpenShift",
    "SaaS",
    "Cloud Security",
    "Hive Metastore",
    "Iceberg",
    "Hudi",
    "Presto"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Multi-Cloud / Hybrid Cloud Engineer",
    "id": 39,
    "rationale": "Domain=Cloud Engineering \u2192 sub-role hybrid-cloud-developer; The JD centers on building and operating a Lakehouse SaaS across multiple hyperscalers with Kubernetes, CI/CD, and cloud platform experience, which best fits a multi-cloud/hybrid cloud engineer.",
    "role_archetype": null,
    "slug": "multi-cloud-hybrid-cloud-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Agile",
      "tag": "in_db"
    },
    {
      "skill": "Jenkins",
      "tag": "in_db"
    },
    {
      "skill": "Artifactory",
      "tag": "new"
    },
    {
      "skill": "Kubernetes",
      "tag": "in_db"
    },
    {
      "skill": "OpenShift",
      "tag": "new"
    },
    {
      "skill": "Amazon Web Services",
      "tag": "in_db"
    },
    {
      "skill": "IBM Cloud",
      "tag": "in_db"
    },
    {
      "skill": "Microsoft Azure",
      "tag": "in_db"
    },
    {
      "skill": "Google Cloud Platform",
      "tag": "in_db"
    },
    {
      "skill": "SaaS",
      "tag": "new"
    },
    {
      "skill": "Cloud Security",
      "tag": "new"
    },
    {
      "skill": "Go",
      "tag": "in_db"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "Ruby",
      "tag": "in_db"
    },
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          "source": "db"
        },
        "dimension_id": 64,
        "input_skill": "Google Cloud Platform",
        "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": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          },
          {
            "display_name": "Cyber Security Engineer",
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            "rationale": null,
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            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 425,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
        "dimension": {
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          "display_name": "Vendor Product Families",
          "id": 477,
          "rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
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          "source": "db"
        },
        "dimension_id": 477,
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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        "roles_from_db": [
          {
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Scripting \u0026 DSL Languages",
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          "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
          "slug": "cloud-security-scripting-dsl-languages",
          "source": "db"
        },
        "dimension_id": 248,
        "input_skill": "Go",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
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        "skipped_reason": null
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      {
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          "difficulty_hint": "well_known",
          "display_name": "Go Language and Toolchain",
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          "rationale": "Core Go implementation skills used to build backend service logic, handlers, and internal libraries. This is the primary coding surface for the role and includes language features, module management, and compiler/runtime behavior relevant to service code.",
          "slug": "go-language-and-toolchain",
          "source": "db"
        },
        "dimension_id": 367,
        "input_skill": "Go",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Go Backend Developer",
            "id": 81,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "go-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 3,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
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          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
          "slug": "programming-languages",
          "source": "db"
        },
        "dimension_id": 1,
        "input_skill": "Go",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
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            "slug": "full-stack-engineer",
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        "skill_tag": "in_db",
        "skipped_reason": null
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      {
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          "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
          "slug": "programming-languages-and-scripting",
          "source": "db"
        },
        "dimension_id": 59,
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cyber Security Engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 3,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
          "id": 39,
          "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 39,
        "input_skill": "Go",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
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            "rationale": null,
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            "display_name": "MLOps Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
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        "skill_dimension_saved": true,
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          "difficulty_hint": "well_known",
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          "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
          "slug": "cloud-security-scripting-dsl-languages",
          "source": "db"
        },
        "dimension_id": 248,
        "input_skill": "Python",
        "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": "Cloud Security Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
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          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
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          "source": "db"
        },
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        "input_skill": "Python",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
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            "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",
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          },
          {
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            "slug": "full-stack-engineer",
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          },
          {
            "display_name": "Fullstack Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages \u0026 DSLs",
          "id": 475,
          "rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
          "slug": "programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 475,
        "input_skill": "Python",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Engineering Manager",
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            "rationale": null,
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            "slug": "engineering-manager",
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
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        "skipped_reason": null
      },
      {
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        "dimension": {
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          "display_name": "Programming Languages and Scripting",
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          "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
          "slug": "programming-languages-and-scripting",
          "source": "db"
        },
        "dimension_id": 59,
        "input_skill": "Python",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cyber Security Engineer",
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            "rationale": null,
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            "slug": "cybersecurity-engineer",
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        ],
        "skill_dimension_saved": true,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
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          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
          "id": 39,
          "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 39,
        "input_skill": "Python",
        "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": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
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            "rationale": null,
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            "slug": "ml-ops-engineer",
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for XR",
          "id": 97,
          "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
          "slug": "programming-languages-for-xr",
          "source": "db"
        },
        "dimension_id": 97,
        "input_skill": "Python",
        "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": "AR/VR Engineer",
            "id": 8,
            "rationale": null,
            "role_archetype": null,
            "slug": "ar-vr-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Python Programming",
          "id": 290,
          "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
          "slug": "python-programming",
          "source": "db"
        },
        "dimension_id": 290,
        "input_skill": "Python",
        "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": "Python Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages and Scripting",
          "id": 59,
          "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
          "slug": "programming-languages-and-scripting",
          "source": "db"
        },
        "dimension_id": 59,
        "input_skill": "Ruby",
        "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": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 300,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Ruby Language and Syntax",
          "id": 415,
          "rationale": "Core Ruby implementation skills used to build backend business logic, helpers, and service internals. This is the primary coding surface for the role and includes idiomatic Ruby used in production codebases.",
          "slug": "ruby-language-and-syntax",
          "source": "db"
        },
        "dimension_id": 415,
        "input_skill": "Ruby",
        "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": "Ruby Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "ruby-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 300,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
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          "display_name": "React Frontend Development",
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          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 96,
        "input_skill": "Git",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 1002,
        "skill_tag": "in_db",
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      },
      {
        "chosen_role_id": 39,
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          "difficulty_hint": "well_known",
          "display_name": "CI/CD Pipeline Platforms",
          "id": 150,
          "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
          "slug": "ci-cd-pipeline-platforms",
          "source": "db"
        },
        "dimension_id": 150,
        "input_skill": "GitHub",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "DevOps Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "devops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 280,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "CI/CD for Machine Learning",
          "id": 56,
          "rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
          "slug": "ci-cd-for-machine-learning",
          "source": "db"
        },
        "dimension_id": 56,
        "input_skill": "GitHub",
        "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": "ML Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 280,
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        "skipped_reason": null
      },
      {
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        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "React Frontend Development",
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          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 96,
        "input_skill": "RDBMS",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 1355,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Model and Data Versioning",
          "id": 48,
          "rationale": "Versioning systems for datasets, features, and model artifacts at the storage layer. This enables reproducible training, rollback, lineage of artifacts, and controlled promotion of model assets.",
          "slug": "model-and-data-versioning",
          "source": "db"
        },
        "dimension_id": 48,
        "input_skill": "Delta Lake",
        "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": "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"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 237,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 39,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Spark",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1350,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 0
  },
  "planner_output": null,
  "run_id": "5c88d2ac-228b-4958-b101-a3008380b54c"
}

LLM Calls

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

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