Analytics Engineer

Enhesa is the leading provider of regulatory and sustainability intelligence worldwide. As a trusted partner, we empower the global business community with the insight to act today and prepare for tomorrow to create a more sustainable future - positively impacting our environment, our health, our safety, and our future. Navigating the fast-changing compliance and sustainability landscapes, we help them understand not just what they should do (first) but also how to do it. Both in their unique business and anywhere in the world. Now and in the future.

• Identify EHS requirements for the industry
• Provide EHS compliance tools to companies
• Advise companies in developing and implementing corporate EHS strategies

Enhesa’s core clients include Fortune 500 multinational companies. For more information, visit www.enhesa.com

As part of our highly dynamic team, we offer:

• A competitive salary package & benefits with a flexible home-working policy
• Work/life balance and a fast-paced and driven environment
• Accountability and pride for your projects

As an Analytics Engineer at Enhesa, you will own the curated (Gold) analytics layer in Microsoft Fabric - turning raw and semi processed data into trusted, well documented dimensional models and metrics for Power Power BI, self service analytics, and AI enabled use cases. This role bridges data engineering and business intelligence by translating ambiguous business needs into scalable analytical data products.

• →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.

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

• 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.

• 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.