Role Summary
We are seeking an experienced Data Engineering Manager with strong Databricks and modern cloud data engineering expertise to lead the design, development and operation of scalable enterprise data solutions. The role combines technical leadership, people management, architecture oversight and delivery accountability. The successful candidate will establish engineering standards, reusable frameworks and robust practices for secure, governed, reliable and cost-efficient Lakehouse solutions.
Key Responsibilities
Lead, mentor and support a team of data engineers, setting technical direction and encouraging engineering best practices.
Design, build and operate scalable data pipelines and Lakehouse solutions using Databricks, Apache Spark, Delta Lake, SQL, Python and cloud-native services.
Define standards for ingestion, transformation, orchestration, automated testing, deployment, observability, monitoring and production support.
Collaborate with business, analytics, data science, architecture, security and platform teams to translate requirements into high-quality data solutions.
Champion reusable and metadata-driven frameworks, automation, Medallion Architecture patterns and modern data engineering practices.
Embed data quality, security, governance, privacy and compliance controls across pipelines and data products.
Review solution designs, code and deployment approaches to maintain consistency, resilience, maintainability and operational reliability.
Optimize Databricks workloads for performance, scalability, reliability and cost efficiency.
Support Agile delivery, including backlog refinement, sprint planning, estimation, risk management, continuous improvement and technical roadmap development.
Requirements Required Qualifications & Experience
Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, Data Science or a related discipline.
Significant professional experience in data engineering, including experience leading or managing data engineering teams.
Strong hands-on experience with Databricks and Lakehouse-based architectures.
Advanced proficiency in Apache Spark / PySpark, SQL, Python, Delta Lake, ETL/ELT, data pipeline development and orchestration.
Strong understanding of Medallion Architecture, distributed processing and modern cloud data-platform patterns.
Experience implementing CI/CD, automated testing, logging, monitoring, alerting and controlled deployment practices.
Working knowledge of data governance, lineage, data quality, security, privacy and role-based access control.
Proven experience optimizing large-scale workloads for performance, reliability and cost.
Experience conducting design and code reviews and establishing engineering standards.
Strong stakeholder management, problem-solving and written and verbal communication skills.
Preferred Qualifications
Experience with Azure Databricks, Unity Catalog and the broader Azure data ecosystem.
Experience building reusable, configuration-driven or metadata-driven ingestion and transformation frameworks.
Exposure to Azure DevOps, Git-based CI/CD, infrastructure as code and automated release controls.
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