Company
Technology
DataEngineer(Databricks&Azure)|Senior
Neural analysis suggests this role is
optimal for Senior candidates.
“Data Engineer (Databricks & Azure) | Senior. Skills: Databricks, Azure, Lakehouse architecture, Data platforms. Design analytical consumption layer. Structure analytical consumption layer”
Industry & Context.
What They're Looking For.
Must Have
Databricks SQL Warehouse production experience, Lakehouse architecture knowledge, Azure Data Lake Storage experience, Delta Lake proficiency, Power BI expertise, Advanced SQL skills, ETL/ELT data pipelines design, Data governance knowledge, Azure cloud environments experience
Nice to Have
Unity Catalog experience, DataOps practices knowledge, MLOps practices knowledge, Azure Data Factory experience, Data security models familiarity, Access control strategies familiarity
What You'll Do.
Design analytical consumption layer
Structure analytical consumption layer
Maintain analytical consumption layer
Design monitoring layer
Structure monitoring layer
Maintain monitoring layer
Build Databricks SQL Warehouse environments
Manage Databricks SQL Warehouse environments
Ensure separation between layers
Deliver governed datasets
Integrate Databricks with ADLS
Integrate Databricks with Power BI
Manage historical storage
Manage data persistence
Implement data traceability
Implement data auditability
Implement reprocessing capabilities
Define RAW data strategy
Define curated data strategy
Define historical data strategy
Maintain RAW data strategy
Maintain curated data strategy
Maintain historical data strategy
Establish monitoring practices
Improve monitoring practices
Establish governance practices
Improve governance practices
Establish data observability practices
Improve data observability practices
Support business teams
Support analytics teams
Build strategic reporting solutions
How You'll Work.
Team & Collaboration
Business teams; Analytics teams
Full Job Description
## Accountabilities Design, structure, and maintain the analytical consumption and monitoring layer of the data platform. Build and manage Databricks SQL Warehouse environments for scalable and efficient data querying. Ensure proper separation between processing, storage, and analytical consumption layers in a Lakehouse architecture. Deliver governed datasets for Power BI and other enterprise analytics tools. Integrate Databricks with Azure Data Lake Storage (ADLS) and Power BI to enable end-to-end data flows. Manage historical storage and data persistence using Delta Lake, ensuring reliability and scalability. Implement data traceability, auditability, and reprocessing capabilities across pipelines. Define and maintain RAW, curated, and historical data strategies within the Lakehouse model. Establish and improve monitoring, governance, and data observability practices. Support business and analytics teams in building dashboards, KPIs, and strategic reporting solutions. Requirements: Strong hands-on experience with Databricks SQL Warehouse in production environments. Solid knowledge of Lakehouse architecture and modern data platform design. Experience with Azure Data Lake Storage (ADLS). Proficiency in Delta Lake and Delta table management. Strong expertise in Power BI and analytical data modeling. Advanced SQL skills with experience in complex queries and optimization. Experience designing and maintaining ETL/ELT data pipelines. Knowledge of data governance, quality, lineage, and monitoring practices. Experience working in Azure cloud environments. Differentials: Experience with Unity Catalog in Databricks. Knowledge of DataOps and/or MLOps practices. Experience with orchestration tools such as Azure Data Factory. Familiarity with data security models (RBAC/ABAC) and access control strategies. Benefits: Competitive compensation package aligned with senior-level market standards. Remote or hybrid work flexibility depending on project allocation. Access to cutting-ed
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