Databricks Data Engineering Manager: Cloud Analytics Lead

EY; Arlington (VA); Remote; USD 126,000 - 230,000; Full time

Job summary

EY is seeking a dynamic Manager of Data Engineering to lead a team delivering cloud analytics solutions with a strong Databricks focus. You will design scalable data architectures, mentor engineers, and collaborate with clients to translate business needs into technical outcomes.

You’ll work across cloud platforms (AWS, Azure, GCP), ensure data governance, and drive end-to-end data pipelines while staying ahead of trends in data engineering and analytics.

Qualifications

• Bachelor’s degree in computer science, engineering, or related field.
• 4–6 years of data engineering experience with cloud analytics focus.
• Expertise in Databricks and Spark for big data processing.
• Hands-on AI-native data engineering practices and pipeline tooling.
• Strong architecture skills and team leadership experience.
• Proficient in Python, Scala, or SQL and client-facing communication.

Responsibilities

• Lead workstream delivery and ensure quality across processes.
• Engage with clients daily; identify opportunities for services.
• Design, build, and operate scalable data architectures and pipelines.
• Mentor a team of data engineers and foster collaboration.
• Manage budgets, engagement economics, and project risk.
• Drive end-to-end data integration and governance practices.

Skills

Databricks

Cloud architecture

Leadership

Client management

Data governance

Data pipelines

Big data

SQL

Python

Scala

Education

Bachelor’s degree in CS/Engineering

Master’s degree preferred

Tools

Spark

Cursor

GitHub Copilot

Databricks Assistant

Job description

EY is seeking a dynamic Manager of Data Engineering to lead a team delivering cloud analytics solutions with a strong Databricks focus. You will design scalable data architectures, mentor engineers, and collaborate with clients to translate business needs into technical outcomes.

You’ll work across cloud platforms (AWS, Azure, GCP), ensure data governance, and drive end-to-end data pipelines while staying ahead of trends in data engineering and analytics.