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Databricks Engineer / W2 position
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VentureSoft Global
Detroit, MI (In Person)
Full-Time
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Job Description
Lead Data Engineer•
Databricks Data Modeling & Architecture Location:
Detroit, MI (Onsite)Work Arrangement:
100% Onsite Job Summary We are seeking an experienced Databricks Data Engineer for a 6-month onsite contract engagement in Detroit, MI. The ideal candidate will design and maintain enterprise-grade data models and architectures across data warehouses, data lakes, and lakehouse platforms. This role combines hands-on data modeling expertise with architectural leadership, partnering with business and technology teams to deliver scalable, governed, and high-performance data solutions on Databricks and Microsoft Azure. Key Responsibilities Design Enterprise Data Models Develop and maintain conceptual, logical, and physical data models for data warehouses, data lakes, and lakehouse architectures using Databricks and modern data platforms. Architect Scalable Data Solutions Design end-to-end data architectures leveraging Databricks, Delta Lake, Spark, and cloud platforms to support AI/ML and business intelligence initiatives. Establish Data Governance & Standards Define data modeling standards, metadata management, data quality frameworks, and master data management governance processes to ensure trusted and compliant data assets. Lead Data Integration & Optimization Architect and optimize batch, streaming, CDC, and ELT/ETL pipelines, ensuring high performance, scalability, efficiency, and reliability across enterprise data ecosystems. Collaborate with Business & Technology Teams Partner with business stakeholders, data engineers, analysts, and AI/ML teams to translate requirements into scalable data products, reusable data domains, and enterprise-wide data solutions. Required Skills & Qualifications Databricks Lakehouse Architecture- proven hands-on design and implementation experience Delta Lake, Unity Catalog, Lakeflow, Auto Loader PySpark and SQL
- strong development and optimization skills Data Modeling
- 3NF, Dimensional (Star/Snowflake), and Data Vault methodologies Data Governance, Data Quality (DQ), and