Analytics Engineer

Core responsibilities

• Own the end-to-end Silver-to-Gold transformation layer—clarify requirements, define grains and KPIs, implement business logic, and deliver curated datasets to production. - Develop performant SQL and PySpark transformations (CTEs, window functions, MERGE/upserts) with incremental processing, idempotency, and recovery patterns. - Design dimensional models (facts/dimensions, SCD Type 1/2, conformed dimensions) with clearly defined semantics for consistent reporting across domains. - Optimize Gold schemas for Power BI semantic models and ad hoc analytics—reducing downstream DAX/SQL complexity and enabling scalable self-service. - Implement quality and trust controls: validation and reconciliation checks, automated tests, documentation and lineage, and monitoring for data freshness and breaking changes. - Partner with Data Engineers and BI Engineers to align ingestion with consumption; maintain medallion-layer hygiene (partitioning, file sizing, OPTIMIZE/VORDER, schema evolution) in Microsoft Fabric. - Apply strong engineering practices and governance: Git branching, CI/CD checks, environment promotions, runbooks; secure access patterns (RLS/OLS), least privilege, and data classification. - Manage stakeholders proactively—surface risks, negotiate scope/timelines, and communicate trade offs and impact clearly. 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. If you are ready to join our journey, please apply!