Team Lead Data Engineering (Onshore, Managing Hybrid Teams)

This role requires a seasoned data engineering professional to lead a team responsible for migrating legacy data systems to modern platforms. The successful candidate will work onsite in Cincinnati, OH, and collaborate with various stakeholders to ensure seamless delivery and integration of data solutions.

Position Title: Team Lead – Data Engineering (Onshore, Managing Hybrid Teams)
Location: Cincinnati, OH (Onsite)
Duration: Long Term Contract
 
Job description:
This role requires a seasoned data engineering professional to lead a team responsible for migrating legacy data systems to modern platforms. The successful candidate will work onsite in Cincinnati, OH, and collaborate with various stakeholders to ensure seamless delivery and integration of data solutions.
 
Basic Qualifications:

• 8 years of experience in data engineering, data warehousing, ETL/ELT, or analytics engineering.
• 2 years of experience leading onshore/offshore engineering teams.

Required Skills:

• Strong hands-on expertise with Databricks, Apache Spark, SQL, and Python/PySpark.
• Experience with IBM Netezza or similar legacy data warehouse platforms.
• Experience supporting data platform migration or modernization initiatives.
• Familiarity with GCP and Azure cloud platforms.
• Strong understanding of data pipelines, data lakehouse concepts, and large-scale data processing.
• Experience working with global delivery teams and multiple vendor partners.
• Strong communication, problem-solving, and technical troubleshooting skills.

Preferred Qualifications:
Not specified.
Role and responsibilities:

• Lead onshore and offshore data engineering teams across mixed vendor groups, including Capgemini and other partners.
• Drive technical delivery for migration from Netezza to Databricks.
• Translate business and architecture requirements into technical designs and engineering tasks.
• Develop and optimize ETL/ELT pipelines, data transformations, and Databricks workflows.
• Guide conversion of legacy Netezza SQL, scripts, and data processing logic into Databricks solutions.
• Ensure code quality, performance tuning, testing, and adherence to engineering best practices.
• Coordinate with architects, project managers, business stakeholders, and vendor teams.
• Provide mentoring and technical direction to junior and mid-level data engineers.
• Participate in code reviews, issue resolution, deployment, and production support.
• Support data validation, reconciliation, cutover planning, and post-migration stabilization.