AI Data Engineering Manager (Hybrid)

EY; Secaucus (NJ); Hybrid; USD 126,000 - 230,000; Full time

Benefits offered by this job

Competitive compensation

Flexible hybrid work model

Total Rewards package including health

Retirement plans

Job summary

EY is seeking a Data Engineering Manager in the US to design and deliver AI‑driven analytics platforms that create measurable business value. You’ll lead cross‑functional teams of data engineers, scientists and developers, translating complex requirements into scalable data architectures.

In this hybrid role, you’ll engage with clients, manage delivery across workloads, and mentor talent while navigating changing priorities.

Qualifications

• Bachelor’s degree required in Data Science, ML, CS, Statistics, Mathematics, Engineering, Applied Math, Analytics, or related preferred.
• Typically, no less than 4 - 6 years relevant experience.
• Hands-on experience developing scalable data solutions using Python, R, and SQL.
• Cloud Computing Architecture.
• Data Architecture Design and Modelling.
• Data Integration.
• Data Security.
• Ontology Design.
• Software Engineering.
• Data Quality.
• Database Management.
• Semantic Layer.
• Building and Managing Relationships.
• Client Trust and Value.
• Communicating With Impact.
• Hybrid Collaboration.
• Commercial Acumen.
• Leading Teams.
• Managing Change.

Responsibilities

• Design and build AI/analytics solutions delivering business value.
• Collaborate with data professionals, management, and stakeholders.
• Create scalable data architectures and support the data asset lifecycle.

Skills

Python

R

SQL

Cloud computing

Data architecture

Data modeling

Data integration

Data security

Machine learning concepts

Communication

Project management

Education

Bachelor’s degree in Data Science, CS, Statistics, Math, Eng, Analytics or related

Job description

EY is seeking a Data Engineering Manager in the US to design and deliver AI‑driven analytics platforms that create measurable business value. You’ll lead cross‑functional teams of data engineers, scientists and developers, translating complex requirements into scalable data architectures.

In this hybrid role, you’ll engage with clients, manage delivery across workloads, and mentor talent while navigating changing priorities.