Sr. Data Engineer

Requirements:

• 5-7 years of data engineering experience delivering production data products at enterprise scale.
• Hands-on experience with modern Lakehouse platforms (Delta Lake, Unity Catalog, PySpark, SQL); strong performance tuning and tiered data product design.
• Hands-on experience with cloud data warehousing (SQL, secure views, role-based access, warehouse optimization).
• Strong SQL fundamentals: query optimization, set-based transformations, reconciliation patterns, SCD Type 1 and Type 2
• Cloud data integration and orchestration experience (data integration platforms, event streaming, enterprise scheduling tools).
• Working understanding of Power BI and downstream consumption enough to design data products that reporting can actually use.
• Familiarity with Oracle ERP/EPM data flows and integration points (GL, sub-ledger accounting, AP, AR, CM, FA, Projects; FCCS, ARCS, EDMCS, EPRCS).
• Demonstrated ability to operate autonomously on production data products from intent through hypercare without day-to-day direction.
• Customer-minded engineering: builds with the downstream consumer and business outcome in mind; asks the right questions before writing code; uses sound judgment on speed vs. durability.
• Drives clarity in ambiguity; surfaces blockers early, names decisions, identifies owners, and proposes a path forward.
• Pragmatic, direct communication, clear and concise in writing and standups; able to explain technical decisions to non-engineers.
• Agile delivery experience (Jira, Confluence, CI/CD).
• Must-have skills: Azure Data Factory, Azure Databricks, Microsoft Fabric, Delta Lake, SQL, PySpark, Python, DBT (Data Build Tool), Medallion Architecture, Real-time Streaming Pipelines, Data Modelling, ETL/ELT Pipelines, Control-M, CI/CD, GITLAB.

Nice-to-Have:

• Kafka, Azure Event Hub, Deepio.
• Familiarity with Agile / Scrum.
• AI Tools (Claude, Copilot, etc. ) and AI/ML-ready data preparation.
• Familiarity with USGCI and SOX.
• Alteryx/Power BI.
• Background in finance, procurement, network infrastructure, or credit risk data domains: Senior Data Engineer.