Analytics Engineer

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 layerDesign and build dbt models that turn raw sources into trusted curated dataApply dimensional modeling to shape facts and dimensions for analyticsModel transactional sources that originate from CDC and third normal form patternsDefine and enforce naming conventions and project structureThe data consumption layerBuild and maintain data products and datasets for internal stakeholdersDeliver clear, well-designed reporting in Metabase and PigmentTranslate business questions into data models with stakeholdersEnable safe, governed data sharing across teamsData in productionOwn the health of production pipelines orchestrated in Databricks or Apache AirflowSet up monitoring, alerting, and data quality checks that catch issues earlyManage CI/CD pipelines, schema migrations, and safe releasesTune performance and cost of workloads on columnar enginesDevelopment patterns, documentation, and governanceEstablish reusable development patterns and code review standardsMaintain model documentation, lineage, and column-level metadataContribute to governance standards and data contracts with stakeholders and source teamsSupport vendor and third-party data ingestion as a secondary focusWhat you bring:Data modeling and transformationProven experience with dimensional modeling for analytics warehousesHands-on production experience with dbtUnderstanding of CDC and third normal form modeling for MySQL or PostgreSQL sourcesExperience with lakehouse architecture and Delta LakeEngineering practiceStrong SQL and confident, practical PythonComfort with Git, the Linux shell, and code review workflowsExperience structuring and orchestrating workflows in Databricks or Apache AirflowProduction experience with CI/CD, migrations, monitoring, and alertingPlatform and performanceExperience with AWS and DatabricksDemonstrated ability to model for and performance-tune columnar enginesExperience loading data from vendors and third-party sourcesExperience with data quality processes; familiarity with data contracts and semantic layers is a plus.Working with stakeholdersVisualization experience with Metabase, Pigment, or similar toolsAbility to explain technical trade-offs clearly to non-technical colleaguesPlans and delivers complex work independently, with minimal supervisionRaises issues early and comes with proposed solutionsWhat success looks likeStakeholders trust the curated data and use it without reconciling numbers themselvesProduction pipelines run reliably, and issues surface through alerts rather than stakeholder reportsNew data products ship through a consistent, tested, and documented patternModels are documented well enough that others can use and extend them confidentlyWorkloads stay fast and cost-efficient as data volumes growReporting moves off ad-hoc queries and onto the governed data layerImprovements to the data platform are proposed and driven proactively, not only delivered on requestWhy this role mattersDecisions across Uberall depend on consistent, trustworthy data, and this role is where that consistency is builtA strong transformation layer lets the business scale reporting without scaling manual effortThe patterns set here become the foundation the rest of the data team builds onAI and innovation expectationsUse AI coding assistants to write, test, refactor, and document SQL, dbt, and Python, with human review built into the quality processExplore AI to automate documentation, data quality checks, and self-serve analytics, so stakeholders get answers faster