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About the Role
Design and deliver end-to-end data products for assigned business domains across the bronze, silver, and gold layers, including dimensional and semantic models for reporting and AI.
Responsibilities
- Design and deliver end-to-end data products across bronze, silver, and gold layers, including dimensional and semantic models.
- Build robust ingestion from enterprise systems like Oracle Fusion, Hyperion, CMiC, project and operations systems, SharePoint, and Power Platform using various patterns.
- Lead workstreams within the Synapse Analytics to Databricks migration, including re-engineering pipelines, running parallel loads, reconciling results, and preparing for cutover.
- Contribute reusable frameworks, templates, and patterns, and propose improvements to standards.
- Implement data quality frameworks, monitoring, and reconciliation, and drive issues to resolution at source.
- Implement access control, lineage, classification, and data-handling rules in Unity Catalog and Microsoft Purview, including PDPL controls.
- Partner with the Data Governance Office to translate domain policies into working controls and certified datasets.
- Tune Spark and SQL workloads for performance and cost within the platform's cluster and workload policies.
- Own the operational health of domains, including monitoring, alerting, incident response, and root-cause fixes.
- Apply CI/CD, automated testing, and DataOps practices using Git and Azure DevOps.
- Build AI-ready data products, including feature tables and document and vector pipelines, for machine learning, RAG, and agent use cases.
- Deliver Power BI semantic models and certified datasets, supporting self-service adoption.
- Apply AI-assisted engineering for code generation, testing, documentation, and lineage discovery with human review.
- Translate requirements from 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.
Requirements
- 5+ years of hands-on data engineering experience.
- At least 2 years of experience with Databricks, Apache Spark, and Delta Lake in production.
- Experience designing and delivering 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.
- Experience implementing data quality and access controls using Unity Catalog, Microsoft Purview, or equivalent tooling.
- Strong SQL, Python, and PySpark skills.
- Experience with Git-based CI/CD delivery (Azure DevOps or equivalent).
- Excellent written and verbal communication skills across technical and business audiences.
- Databricks Certified Data Engineer Associate or Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) — within 6 months of joining.
- Databricks Certified Data Engineer Professional — within 12 months of joining.
Skills
- Databricks
- Apache Spark
- Delta Lake
- Oracle Fusion
- Hyperion
- CMiC
- SharePoint
- Power Platform
- Synapse Analytics
- Unity Catalog
- Microsoft Purview
- Spark
- SQL
- Git
- Azure DevOps
- Python
- PySpark
- Power BI
- Generative AI
- RAG
- AI Engineering
Experience Level
- 5+ years of hands-on data engineering experience
- At least 2 years with Databricks, Apache Spark, and Delta Lake in production
Education Level
- Databricks Certified Data Engineer Associate or Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750)
- Databricks Certified Data Engineer Professional
- 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)