Databricks Manager

About This Role

This role involves overseeing the design, deployment, and optimization of enterprise-scale data engineering solutions using Databricks on major cloud platforms at Deloitte. The Manager will lead innovation in big data architecture, shape best practices, advise senior stakeholders, and ensure data solutions align with business objectives.

Responsibilities

• Lead the development, implementation, and scaling of advanced data engineering solutions using Databricks across AWS, Azure, or GCP environments
• Establish, document, and promote best-in-class approaches for data architecture, integration, and modelling
• Oversee the design, development, and maintenance of robust data pipelines and data architectures
• Initiate and manage efforts to improve data quality, operational efficiency, and process scalability
• Evaluate, pilot, and integrate new big data and analytics technologies
• Consult on, design, and implement governance, security, and compliance strategies for cloud data ecosystems
• Lead, coach, and develop teams of data engineers and architects, fostering technical growth and effective project delivery
• Communicate technical concepts and business value to diverse stakeholders
• Oversee the implementation of CI/CD practices with tools such as Azure DevOps, AWS Code Pipeline, Jenkins, TFS, or PowerShell

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field
• 6+ years of hands-on experience in data engineering with a strong focus on Databricks on AWS, Azure, or GCP
• Expertise with cloud-native databases, storage solutions, and distributed compute platforms
• Deep understanding of Lakehouse architecture, Apache Spark, Delta Lake, and related big data technologies
• Advanced skills in data warehousing, 3NF, dimensional modeling, and enterprise-level data lakes
• Experience with Databricks components including Delta Live Tables, Autoloader, Structured Streaming, Databricks Workflows, and orchestration tools (e.g., Apache Airflow)
• Expertise in designing and supporting incremental data loads and building metadata-driven ingestion/data quality frameworks using PySpark
• Hands-on experience with Databricks Unity Catalog and implementing fine-grained security and access control
• Proven track record in deploying code and solutions via automated CI/CD pipelines
• 4+ years of experience leadership in managing complex, cross-functional data projects and technical teams
• Experience with performance optimization of Data engineering pipelines, code, compute resources
• Ability to travel up to 50%

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field
• 6+ years of hands-on experience in data engineering with a strong focus on Databricks, deployed on any major cloud (AWS, Azure, GCP), and 4+ years of leadership experience managing complex, cross-functional data projects and technical teams.

Nice to Have

• Master's degree
• Comprehensive knowledge of the AWS, Azure, and GCP cloud ecosystems and associated big data stacks
• Demonstrated skill in performance tuning and optimization within Databricks/Apache Spark environments
• Stays current with the latest Databricks feature releases and platform enhancements
• Exceptional communication and stakeholder management abilities
• Experience with Databricks Lakeflow
• Experience in AI/ML

Skills

* Required skills