Senior Data Analytics Engineer

About the role

As part of the Telefonica Tech Data Office, the Senior Data Analytics Engineer designs, builds and continuously improves analytics data products across the full platform lifecycle. The role turns business requirements into production-grade data models and reports, and leads a small group of engineers, setting direction, growing capability and fostering a culture of quality and collaboration.

Key responsibilities

• Build ingestion pipelines across sources using ADF and Databricks orchestration patterns, and optimise data transformations in Databricks including fact/dimension modelling for data marts.
• Implement metadata-driven engineering practices and support CI/CD delivery, release hardening and onboarding of new sources.
• Maintain a business data ontology with canonical entities, relationships and shared vocabulary.
• Partner with data product owners and reporting teams to evolve semantic models and ensure curated data matches reporting needs.
• Build trusted relationships with stakeholders, translate ambiguous business problems into technical requirements, run requirements workshops and communicate progress and risks proactively.
• Lead a small engineering team: set expectations on standards and code quality, run sprint planning, standups, retrospectives and design discussions, and mentor engineers through pair programming, reviews and stretch assignments.

Required profile

• Platform ownership mindset, taking end-to-end accountability from ingestion to consumption.
• Analytical engineering depth: turning business requirements into maintainable data models and performant transformations.
• Data quality discipline: designing for validation, testability, lineage awareness and predictable operation.
• Collaboration and influence across engineering, analytics and business stakeholders.
• Senior execution: balancing speed and rigor, improving existing patterns and raising team capability.

Required skills

• Python, PySpark, Spark SQL, SQL (T-SQL) and Delta Lake patterns.
• Databricks notebooks and workflows, Azure Data Factory pipelines, and metadata-driven orchestration.
• Data warehouse modelling: fact/dimension design, slowly changing dimensions and schema evolution.
• Azure DevOps pipelines (YAML), Git workflows and code review standards.
• Power BI / Fabric semantic models and report delivery workflows.