Manager, Data Engineering

Key Responsibilities

•    Lead, coach, and develop a team of data engineers, establishing clear expectations for delivery, technical quality, documentation, and production support.
•    Own the planning and delivery of data engineering initiatives across assigned business domains, balancing modernization, integration, operational support, and stakeholder priorities.
•    Translate complex business requirements into scalable, reliable, and maintainable data pipelines, models, and data products.
•    Provide hands-on technical leadership through architecture reviews, code reviews, troubleshooting, and guidance on Databricks, Spark, SQL, and lakehouse design.
•    Establish and enforce engineering standards for bronze, silver, and gold data layers, data modeling, reusable frameworks, testing, source control, and CI/CD.
•    Lead the migration and integration of data from legacy platforms, SAP, Salesforce, operational databases, APIs, and file-based processes into the AWS Databricks environment.
•    Ensure data solutions include appropriate quality controls, reconciliation, observability, lineage, security, RBAC, and production monitoring.
•    Partner with business, analytics, architecture, application, and infrastructure teams to clarify requirements, manage dependencies, and communicate delivery risks and tradeoffs.
•    Maintain continuity for business-critical reporting and operational data processes during system migrations and organizational change.
•    Promote ownership and accountability throughout the full engineering lifecycle, including requirements, development, deployment, documentation, incident resolution, and knowledge transfer.

Qualifications

Required Qualifications
•    Bachelor’s degree in computer science, engineering, information systems, data science, or a related field, or equivalent professional experience.
•    Significant experience in data engineering, including experience designing and supporting production data pipelines.
•    Previous experience leading or managing data engineers, technical teams, or major data delivery workstreams.
•    Strong hands-on experience with Databricks, Apache Spark, PySpark, and SQL.
•    Experience building data solutions using lakehouse or medallion architecture patterns.
•    Strong understanding of dimensional modeling, relational modeling, data warehousing, and analytical data product design.
•    Experience working with cloud data platforms, preferably AWS.
•    Experience with Git-based development, pull requests, code reviews, CI/CD, and controlled production deployment.
•    Experience integrating data from enterprise applications, relational databases, APIs, and file-based sources.
•    Demonstrated ability to operate effectively in environments with ambiguity, changing priorities, legacy systems, and complex dependencies.
•    Strong written and verbal communication skills.
•    Ability to work directly with business stakeholders and translate business processes into technical requirements.

Preferred Qualifications
•    Experience with AWS services such as S3, IAM, networking, Secrets Manager, Lambda, or related data services.
•    Experience with Databricks Unity Catalog, Workflows, Delta Lake, Delta Live Tables or Lakeflow Declarative Pipelines, and Databricks Asset Bundles.
•    Experience with SAP data, including SAP ECC, S/4HANA, HANA, finance, supply chain, or operational data domains.
•    Familiarity with Python software engineering practices, reusable framework development, and automated testing.

Keys to Success

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