Data Engineering Manager

Join to apply for the Data Engineering Manager role at Lynn Rodens .

Hybrid onsite 2 days/week or 8 days/month. One interview will be onsite (2 interview process).

Databricks is a must. AWS or Azure. Must be able to do code reviews of Python code. Create designs and validate code. "own" the architecture.

• Architect, implement, and optimize end-to-end data solutions on Databricks while integrating with core AWS services.
• Lead a technical team of data engineers, ensuring best practices in performance, security, and scalability.
• Demonstrate deep, hands-on understanding of Databricks internals with a track record of delivering large-scale data platforms in a cloud environment.
• Lead and maintain the Databricks Lakehouse platform, ensuring optimal performance and data versioning with Delta Lake.
• Manage and optimize Databricks infrastructure including cluster lifecycle, cost optimization, and integration with AWS services (S3, Glue, Lambda).
• Design and implement scalable ETL/ELT frameworks and data pipelines using Spark (Python/Scala), including streaming where needed.
• Drive performance tuning of Spark jobs, cluster configurations, and I/O for large-scale data processing.
• Implement security and governance using Unity Catalog to ensure compliance with standards and internal policies.
• Lead and mentor data engineering teams, conduct code reviews, and champion Agile practices while serving as technical liaison across departments.
• Establish monitoring for data pipelines with SLAs, KPIs, and alerting mechanisms.
• Configure and manage end-to-end CI/CD workflows with source control, automated testing, and versioning.

• Bachelor’s Degree in Engineering, Computer Science, or equivalent.
• 5+ years of hands-on experience with Databricks and Apache Spark, building and maintaining production-grade data pipelines.
• Proven experience leading data engineering teams in complex, fast-paced environments.
• Extensive experience with AWS services (S3, EC2, Glue, EMR, Lambda, Step Functions).
• Proficient in Python (PySpark) or Scala, and strong SQL for analytics and data modeling.
• Experience with infrastructure as code using Terraform or AWS CloudFormation.
• Strong background in data warehousing, dimensional modeling, and RDBMS (e.g., Postgres, Redshift).
• Version control (Git) and CI/CD pipelines with automated testing and deployment.
• Excellent communication and stakeholder management skills; ability to translate technical concepts to business terms.
• Experience applying AI in the development lifecycle.
• Some travel may be required to the US.
• Knowledge of the financial industry is preferred.

• Seniority level: Mid-Senior level
• Employment type: Full-time
• Job function: Engineering and Information Technology
• Industries: Information Services

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