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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in architecting and maintaining enterprise data models, including Kimball dimensional models and advanced semantic layers. Proficient in driving data governance, quality, and performance optimization while mentoring engineering teams.
Highest-signal resume keywords
Enterprise Data ModelingDimensional Data ModelingDbt ProficiencySQL Performance TuningData Governance
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data Transformation WorkflowsKimball MethodologyStar SchemaAdvanced Git WorkflowsCI/CDSQLPythonLookerTableauJSON Data Modeling
Soft Skills
Strategic Problem SolvingAttention to DetailExceptional CommunicationSelf-Directed Leadership
Tools & Technologies
SnowflakeGoogle AnalyticsCRM SystemsSalesforceBI Platforms
Industry Keywords
Data EngineeringAnalytics EngineeringBusiness IntelligenceData GovernanceSemantic Layer
Tech Stack
Tools & technologiesPythonSnowflakeSQLTableau
About the role
Key responsibilities & impact- Architect, build, and scale enterprise data models
- Translate complex raw data into clean, business-ready datasets
- Drive data accuracy, documentation, observability, semantic consistency, and warehouse performance
- Lead the design and maintenance of modular dbt models, semantic layers, and BI views
- Partner with executive stakeholders to translate business strategy into analytics architecture
- Architect and maintain Kimball dimensional models, metric definitions, warehouse transformation logic, and scalable BI datasets
- Design and govern production-grade data models powering the enterprise semantic layer and AI/ML integrations
- Establish data quality, automated testing, lineage tracking, cost optimization, and proactive alerting across dbt pipelines
- Refactor legacy business logic and fragmented query architectures into scalable semantic pipelines
- Drive data governance, metric standardization, secure access controls, and single-source-of-truth definitions
- Mentor junior and mid-level engineers
- Lead analytics engineering initiatives, root-cause analysis, architectural enhancements, critical platform workstreams, domain models, and technical roadmap development
- Report to the Manager, Data Engineering (Core)
Requirements
What you’ll need- Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field (or equivalent practical experience)
- 6+ years of experience in analytics engineering, data engineering, or enterprise data modeling
- Proven track record of architecting, deploying, and maintaining mission-critical, production-grade data transformation workflows and enterprise semantic models
- Expertise in dimensional data modeling techniques, including Star Schema and Kimball methodology
- Experience with dbt and advanced semantic layer tools
- Extensive experience modeling complex business domains, product usage metrics, and unstructured/semi-structured JSON data for enterprise BI consumption
- Strong software engineering background applied to data, including advanced Git workflows, CI/CD, modular architecture design, and comprehensive code documentation
- Advanced proficiency in SQL, dbt macros/packages/performance tuning, Snowflake, Python, Git, and modern BI platforms such as Looker or Tableau
- Deep expertise with Google Analytics, marketing/ad/social media platforms, CRM/Salesforce systems, and custom semantic metrics
- Comprehensive understanding of modern data stack architecture, transformation strategies, semantic layer implementation, automated testing, governance, and BI integrations
- Self-directed technical leadership, strategic problem solving, attention to detail, exceptional communication, metric standardization, performance optimization, and rigorous technical documentation
- Only applications/resumes written in English will be accepted