Analytics engineer for data products

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.

• 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
• 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
• 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
• 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
• 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

• 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
• 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
• 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
• 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
• Будет плюсом: familiarity with data contracts and semantic layers

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