Data Platform Lead
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Key skills for this role
About the Role
Group AMANA is seeking an experienced Data Platform Lead with deep expertise in Databricks, Microsoft Azure, and modern lakehouse architectures. The role involves leading the platform refresh from Synapse Analytics to Databricks, building pipelines, enabling AI/ML, and implementing governance.
Key Skills for This Role
Responsibilities
- Design and deliver end to end Databricks lakehouse components — ingestion, transformation, semantic layer, and serving.
- Set and enforce Delta Lake and medallion architecture standards across bronze, silver, and gold layers.
- Lead the technical execution of the Synapse Analytics to Databricks migration, including parallel run, cutover, and decommissioning.
- Build and maintain robust ingestion and transformation pipelines from Amana's core enterprise systems.
- Apply strong CI/CD, DataOps, and infrastructure as code practices using Git, Azure DevOps, and Terraform or Bicep.
- Build and operate feature store, MLOps, and LLMOps foundations on Databricks.
- Implement lineage, cataloguing, access control, data quality, and PDPL controls inside the platform using Unity Catalog and Microsoft Purview.
- Own FinOps hygiene on the platform: cluster policies, workload tiering, rightsizing, and chargeback readiness.
- Provide hands on technical guidance and code review across the team.
Requirements
- Minimum 7 years in data engineering or data platform work
- At least 3 years hands on with Databricks, Apache Spark, and Delta Lake at enterprise scale
- Deep hands on experience with Azure data stack: Databricks, ADLS, Azure Data Factory, Synapse, Azure SQL, Fabric
- Direct experience integrating heavy ERP sources: Oracle Fusion, Hyperion, CMiC, or equivalent
- Governance shipped to production using Unity Catalog or Microsoft Purview
- Practical FinOps outcomes on cloud spend
- Strong on CI/CD, Git, Azure DevOps, and infrastructure as code (Terraform or Bicep)
- Excellent written and verbal communication skills
- Databricks Certified Data Engineer Professional (or Associate with commitment to achieve Professional within 6 months)
Full Job Posting
Why This Role Matters
- Amana operates across capital intensive sectors where data reliability, cost discipline, and speed of insight are business critical. The enterprise data platform is the backbone of every downstream analytics, AI, and governance initiative — from ERP integrated reporting and predictive models to Agen
Core Responsibilities
- Design and deliver end to end Databricks lakehouse components — ingestion, transformation, semantic layer, and serving.
- Set and enforce Delta Lake and medallion architecture standards across bronze, silver, and gold layers.
- Lead the technical execution of the Synapse Analytics to Databricks migration, including parallel run, cutover, and decommissioning.
- Contribute to and sign off on reference designs, integration patterns, and architecture decision records.
- Build and maintain robust ingestion and transformation pipelines from Amana's core enterprise systems: Oracle Fusion (Finance, HCM, P2P, CRM), Hyperion, CMiC, project and operations systems, SharePoint, and Power Platform.
- Apply strong CI/CD, DataOps, and infrastructure as code practices using Git, Azure DevOps, and Terraform or Bicep.
- Ensure platform reliability through monitoring, alerting, and structured incident response.
- Build and operate feature store, MLOps, and LLMOps foundations on Databricks.
- Enable production grade RAG, agent, and Copilot patterns in partnership with the Data Scientist and AI Engineer.
- Deliver Power BI semantic models, certified datasets, and self service guardrails supporting the Amana Digital Academy and enterprise adoption.
- Implement lineage, cataloguing, access control, data quality, and PDPL controls inside the platform using Unity Catalog and Microsoft Purview.
- Partner with the Data Governance Office to translate live policies into enforceable platform controls.
Required Qualifications
- Minimum of 7 years in data engineering or data platform work, with strong hands on delivery experience.
- At least 3 years hands on with Databricks, Apache Spark, and Delta Lake at enterprise scale. Snowflake or BigQuery experience is acceptable where the CV shows a real Databricks migration.
- Contributed to the build or rebuild of an enterprise data platform serving multiple business domains.
- Deep hands on experience with the Azure data stack: Databricks, ADLS, Azure Data Factory, Synapse, Azure SQL, Fabric.
- Direct experience integrating heavy ERP sources: Oracle Fusion, Hyperion, CMiC, or equivalent estates.
- Governance shipped to production using Unity Catalog or Microsoft Purview, with PDPL or GDPR controls operational, not theoretical.
- Practical FinOps outcomes on cloud spend.
- Strong on CI/CD, Git, Azure DevOps, and infrastructure as code (Terraform or Bicep).
- Excellent written and verbal communication skills across technical and business audiences.
Preferred Qualifications
- Direct experience of a Synapse Analytics to Databricks migration.
- Exposure to MLOps and LLMOps stacks: MLflow, feature store, vector stores, RAG, agent frameworks.
- Familiarity with Power BI semantic models and enterprise BI governance.
- Streaming and event driven experience: Kafka, Event Hubs, Structured Streaming.
- Multi entity, multi country group exposure with mixed data residency considerations.
- Industry experience in construction, engineering services, manufacturing, energy, or logistics.
Certifications
- Mandatory: Databricks Certified Data Engineer Professional (Candidates holding the Associate level will be considered on the condition that Professional is achieved within 6 months of joining.)
- Preferred: Databricks Certified Machine Learning Professional, Databricks Certified Generative AI Engineer Associate, Microsoft Certified: Azure Data Engineer Associate (DP 203 / DP 700), Microsoft Certified: Fabric Analytics Engineer Associate (DP 600), Microsoft Certified: Azure Solutions Architec
Education
- M.Tech / B.E. / B.Tech (Computer Science, Information Systems, IT) / MCA / MCS.
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