Databricks Data Engineering Manager - Cloud Analytics Lead

EY; San Antonio (TX); Remote; USD 126,000 - 262,000; Full time

Benefits offered by this job

Medical and dental coverage

Pension and 401(k) plans

Paid time off

Job summary

EY is seeking a dynamic Manager of Data Engineering to lead our team in designing cloud analytics solutions focused on Databricks. You will design and build scalable data architectures, collaborate with stakeholders, and ensure data governance and security across the data lifecycle.

Travel may be required based on client needs. Ideal candidates have 4–6 years in data engineering, strong Python/Scala/SQL skills, and experience leading teams.

Qualifications

• Bachelor’s degree in computer science, engineering, or related field.
• Typically, no less than 4–6 years relevant data engineering experience.
• Proven expertise in Databricks and Spark for big data processing.
• Hands-on with AI-native data engineering practices and agentic workflows.
• Strong data architecture and cloud analytics design experience.
• Experience leading and mentoring teams.

Responsibilities

• Lead design and delivery of scalable data engineering solutions on cloud platforms.
• Engage with clients daily and identify opportunities for additional services.
• Manage resources, budgets, and engagement economics.
• Mentor and develop a team of data engineers.
• Ensure data quality, governance, and security across lifecycle.

Skills

Databricks

Cloud platforms

Data architecture

Client management

Data governance

End-to-end data pipelines

Python/Scala/SQL

Leadership

Communication

Education

Bachelor’s degree in CS/Engineering

Master’s degree preferred

Tools

Databricks

Spark

Cursor

GitHub Copilot

Databricks Assistant

Job description

EY is seeking a dynamic Manager of Data Engineering to lead our team in designing cloud analytics solutions focused on Databricks. You will design and build scalable data architectures, collaborate with stakeholders, and ensure data governance and security across the data lifecycle.

Travel may be required based on client needs. Ideal candidates have 4–6 years in data engineering, strong Python/Scala/SQL skills, and experience leading teams.