Senior Data Analytics Engineer

Senior Data Analytics Engineer Purpose As part of the Telefonica Tech Data Office, the Senior Data Analytics Engineer is responsible for designing, building, and continuously improving analytics data products across the full platform lifecycle. This role requires both soft skills and technical capability to translate business requirements into production-grade data models and reports, and ensures the business obtains value from the work delivered. As a senior member of the team, this role also carries responsibility for leading a small group of engineers — setting direction, growing capability, and fostering a culture of quality and collaboration. Equally important is the ability to build trusted relationships with business stakeholders, communicate clearly across technical and non-technical audiences, and represent the data platform as a reliable partner to the wider organisation. Responsibilities Technical Delivery Design and build ingestion pipelines across a variety of sources using ADF and Databricks orchestration patterns. Build and optimise data transformations in Databricks, including fact/dimension modelling for data marts. Implement metadata-driven engineering practices that use platform contracts and orchestration metadata to improve consistency, reusability, and scale. Partner with data product owners and reporting teams to evolve semantic models and ensure curated data aligns with reporting requirements. Support CI/CD delivery across environments, and participate in release hardening. Contribute to platform evolution by onboarding new sources, refining deployment templates/workflows, and mentoring engineers on engineering standards. Designing and maintaining a business data ontology with canonical entities, relationships, and shared vocabulary. Stakeholder Engagement & Communication Effective partnership with business stakeholders is as important as technical delivery in this role. Build and maintain trusted relationships with business stakeholders, data product owners, and reporting teams — acting as a credible, approachable point of contact for data platform matters. Translate ambiguous business problems into clear technical requirements, and communicate data solutions back in terms that non-technical audiences can understand and act on. Facilitate requirements-gathering conversations and workshops, asking the right questions to uncover underlying needs rather than surface-level requests. Proactively communicate progress, blockers, and delivery risks to stakeholders before they become issues — setting realistic expectations and following through on commitments. Produce clear, audience-appropriate documentation and updates: from concise summaries to structured status reports. Represent the data engineering team in cross-functional forums, contributing constructively to planning, prioritisation, and design discussions. Team Leadership & Engineering Culture This role leads a small engineering team, with accountability for their day-to-day output, growth, and ways of working. Set clear expectations around engineering standards, code quality, and delivery practices — leading by example through your own work and reviews. Run effective team rituals: sprint planning, standups, retrospectives, and technical design discussions that keep the team aligned, unblocked, and continuously improving. Identify skills gaps across the team and create opportunities for growth — through pair programming, structured review, stretch assignments, and knowledge sharing. Shield the team from unnecessary noise and context-switch, while ensuring they have the business context needed to make good engineering decisions. Competencies The following competencies describe how this role is expected to operate — both technically and as a senior individual contributing to team and organisational effectiveness. Platform ownership mindset: takes end-to-end accountability from ingestion all the way through to consumption. Analytical engineering depth: translates business requirements into maintainable data models and performant transformation logic. Data quality discipline: designs for validation, testability, lineage awareness, and predictable operational behaviour. Collaboration and influence: works effectively across engineering, analytics, and business stakeholders; drives clear technical decisions. Senior execution: balances speed and rigor, improves existing patterns, and raises team capability through mentoring and review. Skills & Technologies Core Data Engineering Python, PySpark, Spark SQL, SQL (T-SQL), Delta Lake patterns. Data warehouse and data mart modeling: fact/dimension design, slowly changing dimensions, schema evolution. Platform Stack Databricks notebooks and workflows; Azure Data Factory pipelines. Metadata-driven orchestration patterns and platform contract design. Analytics Consumption Power BI / Fabric semantic model and report delivery workflows. Analytics-facing schema design; close collaboration with repor…