Azure Databricks Data Engineer

EXL; Warren (MI); On-site; USD 120,000 - 190,000; Full time

Job summary

EXL is seeking a skilled Databricks Engineer with 5+ years of hands-on Databricks experience to design, develop, and optimize scalable data platforms for enterprise analytics and AI initiatives. The role focuses on data engineering, ETL/ELT development, cloud technologies (Azure), and a Lakehouse architecture to unify data across sources.

The candidate will collaborate with analytics and AI teams, implement governance, and optimize Spark/Databricks workloads for performance and cost efficiency.

Qualifications

• Bachelor's or Master's degree in CS/IT/Engineering or related field.
• 9-15 years of experience in Data Engineering and Data Warehousing.
• Minimum 5+ years hands-on with Azure Data Engineering technologies.
• Minimum 4+ years hands-on with Azure Databricks and Spark ecosystem.
• Strong understanding of data lake, lakehouse, and cloud-native architecture patterns.
• Experience handling large-scale structured and unstructured datasets.
• Strong analytical, problem-solving, and troubleshooting skills.

Responsibilities

• Design, develop, and maintain data pipelines using Databricks, Spark, and cloud-native services.
• Build and optimize ETL/ELT workflows for large-scale data.
• Develop data models and implement data quality, validation, and governance frameworks.
• Integrate data from multiple sources into a unified Lakehouse architecture.
• Optimize Spark jobs and Databricks workloads for performance and cost efficiency.
• Implement security controls and data governance using Unity Catalog.
• Collaborate with business, analytics, and AI/ML teams to deliver data products.
• Monitor, troubleshoot, and resolve data pipeline issues.
• Support CI/CD, DevOps, and infrastructure automation practices.
• Maintain technical documentation and best practices.

Skills

Databricks Lakehouse Platform

SQL

Python

ETL/ELT Development

Data Modeling

Data Warehousing

Data Quality & Validation

Streaming & Real-Time Processing

Azure Data Engineering

Spark

Education

Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or related field

Tools

Terraform

GitHub Actions

Azure DevOps

Azure Databricks

Power BI

Snowflake

Job description

Experience: 9-15 Years of IT Experience

Job Summary

We are seeking a skilled Databricks Engineer with minimum of 5+ years of hands-on in Databricks who can design, develop, and optimize scalable data platforms and analytics solutions using the Databricks Lakehouse Platform. The ideal candidate will have expertise in data engineering, ETL/ELT development, cloud technologies preferably Azure, and big data processing to support enterprise analytics and AI initiatives.

Key Responsibilities

• Design, develop, and maintain data pipelines using Databricks, Apache Spark, and cloud-native services.
• Build and optimize ETL/ELT workflows for large-scale structured and unstructured data.
• Develop data models and implement data quality, validation, and governance frameworks.
• Integrate data from multiple sources into a unified Lakehouse architecture.
• Optimize Spark jobs and Databricks workloads for performance, scalability, and cost efficiency.
• Implement security controls, access management, and data governance using Unity Catalog.
• Collaborate with business, analytics, and AI/ML teams to deliver trusted data products.
• Monitor, troubleshoot, and resolve data pipeline issues.
• Support CI/CD, DevOps, and infrastructure automation practices.
• Maintain technical documentation and best practices.

Required Technical Skills

Core Technologies

• Databricks Lakehouse Platform
• SQL
• Python
• ETL / ELT Development
• Data Modeling
• Data Warehousing
• Data Quality & Validation
• Streaming & Real-Time Processing

Governance & Security

• Unity Catalog
• Row-Level Security
• Access Control & Compliance
• Data Governance Frameworks
• Terraform
• GitHub Actions / Azure DevOps

Analytics & AI

• Semantic Layers
• Data Products
• BI Platforms

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or related field.
• 9-15 years of experience in Data Engineering and Data Warehousing.
• Minimum 5+ years of hands-on experience with Azure Data Engineering technologies.
• Minimum 4+ years of hands-on experience with Azure Databricks and Spark ecosystem.
• Strong understanding of data lake, lakehouse, and cloud-native architecture patterns.
• Experience in handling large-scale structured and unstructured datasets.
• Strong analytical, problem-solving, and troubleshooting skills.

Preferred Qualifications

• Experience with Snowflake, Power BI, or Microsoft Fabric.
• Experience in real-time streaming solutions using Kafka/Event Hubs.
• Exposure to Data Governance and Master Data Management initiatives.

Soft Skills

• Strong stakeholder management and communication skills.
• Ability to lead technical initiatives and drive architecture discussions.
• Experience working in Agile/Scrum environments.
• Excellent documentation and presentation skills.
• Strong mentoring and team leadership abilities.