{"id":3249160,"url":"https://alion.io/job/uberall-analytics-engineer","title":"Analytics Engineer","company":{"id":51967,"name":"Uberall","domain":"uberall.com","url":"https://alion.io/company/uberall","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"Analytics","role_family":"Analytics","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["AR","BR","ZA","HU","AM","MK"],"hiring_countries_total":6,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"Git","optional":false},{"name":"Linux","optional":false},{"name":"Metabase","optional":false},{"name":"MySQL","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-21T13:36:12Z","employer_posted_date":"2026-10-21","last_verified_at":"2026-10-28T10:45:43Z","board_verified":true,"closed_at":null,"days_open":7,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":7},"description":"About the role:\nThe 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.\nThe 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.\nWhat you will own:\nThe curated data layer\nDesign and build dbt models that turn raw sources into trusted curated data\n\nApply dimensional modeling to shape facts and dimensions for analytics\n\nModel transactional sources that originate from CDC and third normal form patterns\n\nDefine and enforce naming conventions and project structure\n\nThe data consumption layer\nBuild and maintain data products and datasets for internal stakeholders\n\nDeliver clear, well-designed reporting in Metabase and Pigment\n\nTranslate business questions into data models with stakeholders\n\nEnable safe, governed data sharing across teams\n\nData in production\nOwn the health of production pipelines orchestrated in Databricks or Apache Airflow\n\nSet up monitoring, alerting, and data quality checks that catch issues early\n\nManage CI/CD pipelines, schema migrations, and safe releases\n\nTune performance and cost of workloads on columnar engines\n\nDevelopment patterns, documentation, and governance\nEstablish reusable development patterns and code review standards\n\nMaintain model documentation, lineage, and column-level metadata\n\nContribute to governance standards and data contracts with stakeholders and source teams\n\nSupport vendor and third-party data ingestion as a secondary focus\n\nWhat you bring:\nData modeling and transformation\nProven experience with dimensional modeling for analytics warehouses\n\nHands-on production experience with dbt\n\nUnderstanding of CDC and third normal form modeling for MySQL or PostgreSQL sources\n\nExperience with lakehouse architecture and Delta Lake\n\nEngineering practice\nStrong SQL and confident, practical Python\n\nComfort with Git, the Linux shell, and code review workflows\n\nExperience structuring and orchestrating workflows in Databricks or Apache Airflow\n\nProduction experience with CI/CD, migrations, monitoring, and alerting\n\nPlatform and performance\nExperience with AWS and Databricks\n\nDemonstrated ability to model for and performance-tune columnar engines\n\nExperience loading data from vendors and third-party sources\n\nExperience with data quality processes; familiarity with data contracts and semantic layers is a plus.\n\nWorking with stakeholders\nVisualization experience with Metabase, Pigment, or similar tools\n\nAbility to explain technical trade-offs clearly to non-technical colleagues\n\nPlans and delivers complex work independently, with minimal supervision\n\nRaises issues early and comes with proposed solutions\n\nWhat success looks like\nStakeholders trust the curated data and use it without reconciling numbers themselves\n\nProduction pipelines run reliably, and issues surface through alerts rather than stakeholder reports\n\nNew data products ship through a consistent, tested, and documented pattern\n\nModels are documented well enough that others can use and extend them confidently\n\nWorkloads stay fast and cost-efficient as data volumes grow\n\nReporting moves off ad-hoc queries and onto the governed data layer\n\nImprovements to the data platform are proposed and driven proactively, not only delivered on request\n\nWhy this role matters\nDecisions across Uberall depend on consistent, trustworthy data, and this role is where that consistency is built\n\nA strong transformation layer lets the business scale reporting without scaling manual effort\n\nThe patterns set here become the foundation the rest of the data team builds on\n\nAI and innovation expectations\nUse AI coding assistants to write, test, refactor, and document SQL, dbt, and Python, with human review built into the quality process\n\nExplore AI to automate documentation, data quality checks, and self-serve analytics, so stakeholders get answers faster","description_format":"text","description_chars":4203,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Argentina","iso":"AR","kind":"country"},{"name":"Brazil","iso":"BR","kind":"country"},{"name":"South Africa","iso":"ZA","kind":"country"},{"name":"Hungary","iso":"HU","kind":"country"},{"name":"Armenia","iso":"AM","kind":"country"},{"name":"North Macedonia","iso":"MK","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Search Engine Optimization (SEO)","Digital Marketing","Online Reviews & 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