Sr. Data Engineer

Responsibilities:

• Own data products end-to-end from ingestion through curated consumption layers feeding business reporting, downstream models, and analytics teams.
• Build and maintain pipelines on modern lakehouse platforms (Delta Lake, Unity Catalog, PySpark, SQL) and cloud data warehousing environments.
• Develop and operate cloud data integration pipelines, event-streaming ingestion, and enterprise orchestration jobs.
• Apply SCD Type 1 and Type 2 patterns, schema evolution, and tiered curation (Bronze/Silver/Gold).
• Support integrations with Oracle ERP and EPM platforms (GL, AP, AR, CM, FA, Projects; FCCS, ARCS, EDMCS, EPRCS) for finance and operational data needs.
• Internalize the customer context behind each data product and make engineering decisions consistent with it, without requiring the customer in every conversation.
• Choose the fastest responsible path to meet the immediate need, then partner with the team to decide what should become a durable, reusable data product.
• Apply enterprise data standards by default: encryption of sensitive elements, role-based access, audit logging, and retention rules.
• Document data products and contracts clearly in enterprise collaboration tools and the enterprise catalog.
• Own production quality: monitoring, data quality validation, root-cause analysis, and post-deployment support.
• Raise the right questions early, identify owners, and unblock yourself across cross-team dependencies.
• Share patterns, review peer work, and contribute to the team's reusable standards.

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 ambiguous surfaces, blocks 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.