Data Engineer

Design and deliver end-to-end data products for assigned business domains across the bronze silver and gold layers including the dimensional and semantic models that serve reporting and AI

Build robust ingestion from Amana s enterprise systems Oracle Fusion Finance HCM P2P CRM Hyperion CMiC project and operations systems SharePoint and Power Platform using change data capture incremental and API-based patterns

Lead work streams within the Synapse Analytics to Databricks migration re-engineering pipelines running parallel loads reconciling results and preparing for cutover

Contribute reusable frameworks templates and patterns and propose improvements to the standards owned by the Data Engineering Lead Data Architect

Implement data quality frameworks monitoring and reconciliation across your domains and drive issues to resolution at source with data owners

Implement access control lineage classification and data-handling rules in Unity Catalog and Microsoft Purview including PDPL controls for personal and HR data

Partner with the Data Governance Office to turn domain policies into working controls and documented certified datasets

Tune Spark and SQL workloads for performance and cost within the platform s cluster and workload policies

Own the operational health of your domains monitoring alerting incident response and root-cause fixes

Apply CI CD automated testing and Data Ops practices using Git and Azure Dev Ops

Build AI-ready data products feature tables and document and vector pipelines for machine learning RAG and agent use cases in partnership with the Data Scientist and AI Engineer

Deliver Power BI semantic models and certified datasets with the Data Analyst supporting self-service adoption through the Amana Digital Academy

Apply AI-assisted engineering code generation testing documentation and lineage discovery with appropriate human review and help the team adopt it safely

Translate requirements from finance project and operations stakeholders into technical designs with clear delivery outcomes

Mentor Data Engineers through pairing design reviews and code review

Explain design choices trade-offs and risks clearly to technical and business audiences

• 5+ years of hands-on data engineering experience, including at least 2 years with Databricks, Apache Spark, and Delta Lake in production. Designed and delivered end-to-end data products from ingestion to modelled, consumable data for one or more business domains. Experience integrating complex enterprise sources such as Oracle Fusion, SAP, Microsoft Dynamics, or CMiC, including incremental and API-based extraction. Data quality and access controls implemented in practice using Unity Catalog, Microsoft Purview, or equivalent tooling.
  
  Strong SQL, Python, and PySpark, with Git-based CI/CD delivery (Azure Dev Ops or equivalent). Excellent written and verbal communication skills across technical and business audiences.

• Databricks Certified Generative AI Engineer Associate
• Microsoft Certified:
  Fabric Data Engineer Associate (DP-700)
• Microsoft Certified:
  Fabric Analytics Engineer Associate (DP-600)
• Microsoft Certified:
  Azure Data Engineer Associate (DP-203 retired 2025; accepted if held)

• Databricks Certified Data Engineer Associate or Microsoft Certified:
  Azure Databricks Data Engineer Associate (DP-750) within 6 months
• Databricks Certified Data Engineer Professional within 12 months