We are looking for an
Analytics Engineer
to join our team in
Lisbon
.
Tasks
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.
Requirements
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 (nice-to-have);
Orchestration: experience with Fabric or Azure Data Factory pipelines and dependency management (nice-to-have);
Engineering practices: Git and CI/CD workflows, automated testing and documentation standards (nice-to-have);
Microsoft Fabric: Fabric artifacts, capacities, and Fabric-specific optimizations (VORDER) (nice-to-have);
Python: scripting for data utilities, profiling, and automation (nice-to-have).
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.
Work Arrangement
Hybrid (2x per week at the office)
Offer
Health Insurance;
3 and a half days of leave per year + 22 vacation days;
Unlimited access to Udemy.
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