IT&Data Engineer

IT&Data Engineer

Expertise expected – Strong Data Engineer experience in enterprise environments, with hands-on OneSource 2.0 / OneHouse & DTU practice – Databricks (Spark/SQL, Delta Lake) mandatory; ADF / Azure stack; exposure to Snowflake / Informatica a plus – Solid data modelling for analytics (facts, dimensions, derived metrics) and medallion architecture – Comfortable with config-as-code / GitHub / CI-CD working model; experience integrating ERP / Finance / Commercial data (e.g. SAP) Leadership positioning (technical, not managerial) – Recognized technical reference within the squad; able to coach juniors and justify design choices among competing alternatives – Operates under the guidance of the Chapter Manager — no people-management, capacity or standard-setting accountability Building governed Bronze/Silver/Gold data products in OneHouse and DTUs, and coaching junior engineers — while reporting to the L8 Chapter Manager. “Lead” reflects technical seniority, not a management layer, keeping the role cleanly one level below the Chapter Manager in the chapter structure Technical leadership at squad level – Act as the technical reference for the squad’s data engineering work on OneSource 2.0 – Guide solution build within guardrails; drive design and code quality through peer review – Mentor and upskill junior data engineers; support estimation and technical planning – Contribute to Engineering Decision Records (EDRs) for implementation-level decisions Data ingestion (config-driven) – Configure ingestion using the OneHouse metadata-driven framework: create/modify JSON configuration files (ingestion, Bronze) and deploy via GitHub / GitOps / CI-CD – Onboard sources (SAP/DB2, files, Snowflake, external push/pull) into OneHouse, ensuring reliability, completeness and lineage Transformation & data modelling (core value) – Build Silver (cleaned, harmonized, modelled) and Gold (business aggregations / data products) datasets implementing business KPIs – Apply and enrich reusable logic from the Transformations Library; document and version it – Translate KPI definitions into scalable transformations aligned with domain data models Platform engineering within DTUs – Develop within DTUs using Databricks (Spark/SQL/Delta Lake) and, where relevant, ADF, Snowflake or Informatica – Structure data across Bronze / Silver / Gold; optimize performance, cost (FinOps) and scalability – Apply best practices for CI/CD, IaC, monitoring and data quality checks Governance & data products – Verify integrity of ingested data and entities in Unity Catalog; respect GBDO / Data Owner validation and security-by-design – Ensure only OneHouse-stored, catalogued data is published as shareable, certified data products (SSOT)