Data Engineering Manager, AI & Analytics Platforms

Ernst & Young Oman; Los Angeles (CA); On-site; USD 126,000 - 230,000; Full time

Benefits offered by this job

Hybrid work model

Comprehensive benefits

Job summary

EY is seeking a Data Engineering Manager to design and deliver scalable data architectures and AI-powered analytics solutions for clients across industries. You will collaborate with data scientists, engineers, and management to align technical requirements with business needs and drive value from data assets.

In a hybrid, client-facing environment, you will lead data pipelines, governance, and security while guiding cross-functional teams through implementation, testing, and production

Qualifications

• Bachelor’s degree required in Data Science, Machine Learning, Computer Science, 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, with expertise in data engineering, data pipelines, streaming architectures, and big data ecosystems.

Responsibilities

• Leading workstream delivery and ensuring quality in all processes.
• Engaging with clients on a daily basis, actively participating in working sessions, and identifying opportunities for additional services.
• Implementing resource plans and budgets while managing engagement economics.

Skills

Python

R

SQL

Data engineering

Data pipelines

Streaming architectures

Big data ecosystems

Cloud computing

Data architecture design

Data integration

Data security

Ontology design

Software engineering

Data quality

Database management

Semantic layer

Building relationships

Communicating with impact

Education

Bachelor's degree

Tools

LLMs

GenAI tooling

Job description

EY is seeking a Data Engineering Manager to design and deliver scalable data architectures and AI-powered analytics solutions for clients across industries. You will collaborate with data scientists, engineers, and management to align technical requirements with business needs and drive value from data assets.

In a hybrid, client-facing environment, you will lead data pipelines, governance, and security while guiding cross-functional teams through implementation, testing, and production