Education Level

Bachelor’s degree in Engineering, Computer Science, Information Technology, or a related field (or equivalent practical experience).

Experience

3+ years in Analytics Engineering, Data Engineering, or Business Intelligence, with hands-on delivery of production analytical data models and curated datasets consumed by reporting and/or self-service analytics.

Required Technical Skills

• Advanced SQL: CTEs, window functions, query performance tuning, and reusable transformation logic.
• Dimensional modeling: star schemas, OBTs, fact grain definition, SCD Type 1/2, conformed dimensions, and analytics-ready denormalized patterns experience.
• Spark & Delta Lake: performant transformations (joins, partitioning, skew handling); lakehouse and medallion architecture; Delta features (MERGE, OPTIMIZE, ZORDER, time travel, schema evolution).
• Semantic layer awareness (Power BI): models tables and measures for performant semantic models; collaborates to reduce downstream complexity and align KPI definitions.
• Analytics mindset: translates business questions into metrics and data models; strong understanding of KPI definitions, edge cases, and how definitions impact decisions.
• Data quality & observability: defines checks (completeness/validity/reconciliation), monitors freshness, and troubleshoots data issues through root-cause analysis.
• Data access & governance: implements least-privilege access patterns, RLS/OLS concepts, sensitivity/classification expectations, and safe handling of confidential/PII data.

Nice-to-have Technical Skills:

• Transformation frameworks: dbt (models, tests, documentation) or equivalent patterns.
• Orchestration: experience with Fabric or Azure Data Factory pipelines and dependency management.
• Engineering practices: Git and CI/CD workflows, automated testing and documentation standards.
• Microsoft Fabric: Fabric artifacts, capacities, and Fabric-specific optimizations (VORDER).
• Python: scripting for data utilities, profiling, and automation.

Other Required Skills:

• Communication: explains data semantics to non-technical audiences; surfaces scope/timeline/tech-debt risks early.
• Stakeholder partnership: negotiates constructively; balances competing requests; educates business users without condescension.
• Ownership & autonomy: you build, you own it; anticipates downstream impact on consumers.
• Problem solving depth: decomposes complexity; weighs trade offs; digs for root cause rather than patching symptoms.
• Champion of continuous improvement;
• Language: fluent in English.