Analytics Engineer

Description

Summary: AskBlue is seeking an Analytics Engineer to own end-to-end data transformation, develop performant SQL/PySpark, design dimensional models, and optimize schemas for consistent reporting. Highlights: 1. Own end-to-end Silver-to-Gold data transformation layer 2. Develop performant SQL and PySpark transformations with robust patterns 3. Design dimensional models for consistent reporting across domains Do you know **AskBlue**? We were born in 2013, and we provide services in the field of information technology. We are looking for an **Analytics Engineer** to join our company in one of our projects, 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. If you are interested in the opportunity, upload your C.V. **askblue \- Where Business meets Technology**