Analytics Engineer

Job description

**About the role:** The Analytics Engineer turns raw product, commercial, and vendor data into trusted data products that teams across Uberall use to make decisions. This role owns the transformation layer of our Databricks lakehouse, from curated models through to the datasets and dashboards stakeholders use every day, and keeps it reliable, documented, and fast. The work is analytical and creative in equal measure: data products include visual assets, so strong modeling needs to come with a clear sense of how people will actually read and use the data. ## **What you will own:** ### **The curated data layer** - Design and build dbt models that turn raw sources into trusted curated data - Apply dimensional modeling to shape facts and dimensions for analytics - Model transactional sources that originate from CDC and third normal form patterns - Define and enforce naming conventions and project structure ### **The data consumption layer** - Build and maintain data products and datasets for internal stakeholders - Deliver clear, well-designed reporting in Metabase and Pigment - Translate business questions into data models with stakeholders - Enable safe, governed data sharing across teams ### **Data in production** - Own the health of production pipelines orchestrated in Databricks or Apache Airflow - Set up monitoring, alerting, and data quality checks that catch issues early - Manage CI/CD pipelines, schema migrations, and safe releases - Tune performance and cost of workloads on columnar engines ### **Development patterns, documentation, and governance** - Establish reusable development patterns and code review standards - Maintain model documentation, lineage, and column-level metadata - Contribute to governance standards and data contracts with stakeholders and source teams - Support vendor and third-party data ingestion as a secondary focus ## **What you bring:** ### **Data modeling and transformation** - Proven experience with dimensional modeling for analytics warehouses - Hands-on production experience with dbt - Understanding of CDC and third normal form modeling for MySQL or PostgreSQL sources - Experience with lakehouse architecture and Delta Lake ### **Engineering practice** - Strong SQL and confident, practical Python - Comfort with Git, the Linux shell, and code review workflows - Experience structuring and orchestrating workflows in Databricks or Apache Airflow - Production experience with CI/CD, migrations, monitoring, and alerting ### **Platform and performance** - Experience with AWS and Databricks - Demonstrated ability to model for and performance-tune columnar engines - Experience loading data from vendors and third-party sources - Experience with data quality processes; familiarity with data contracts and semantic layers is a plus. ### **Working with stakeholders** - Visualization experience with Metabase, Pigment, or similar tools - Ability to explain technical trade-offs clearly to non-technical colleagues - Plans and delivers complex work independently, with minimal supervision - Raises issues early and comes with proposed solutions ## **What success looks like** - Stakeholders trust the curated data and use it without reconciling numbers themselves - Production pipelines run reliably, and issues surface through alerts rather than stakeholder reports - New data products ship through a consistent, tested, and documented pattern - Models are documented well enough that others can use and extend them confidently - Workloads stay fast and cost-efficient as data volumes grow - Reporting moves off ad-hoc queries and onto the governed data layer - Improvements to the data platform are proposed and driven proactively, not only delivered on request ## **Why this role matters** - Decisions across Uberall depend on consistent, trustworthy data, and this role is where that consistency is built - A strong transformation layer lets the business scale reporting without scaling manual effort - The patterns set here become the foundation the rest of the data team builds on ## **AI and innovation expectations** - Use AI coding assistants to write, test, refactor, and document SQL, dbt, and Python, with human review built into the quality process - Explore AI to automate documentation, data quality checks, and self-serve analytics, so stakeholders get answers faster