Data Engineer - Manager - Consulting - Location OPEN

EY•Hartford, DC

6d•$125,500 - $261,600•Hybrid

About The Position

The Products and Platforms Practice specializes in mission-critical, intelligent platforms for the world’s most ambitious organizations. Using our product-driven, AI-centric approach, we empower organizations to build for the intelligent digital future. Our elite team of product leaders, data scientists, designers, and software engineers enable our clients to solve their most complex product challenges and positively impact people and the world. Engineers are problem solvers. While most have deep expertise in specific areas of the technology stack (front end, back end, data, security etc.), all are comfortable swimming across lanes. This is really important in delivering the product and platform outcomes that our customers expect. In this role, you will design and build AI and analytics solutions that deliver significant business value. You will collaborate with other data and analytics professionals, management, and stakeholders to ensure that technical requirements align with business needs. Your responsibilities will include creating scalable data architecture and modeling solutions that support the entire data asset lifecycle.

Requirements

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.
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
Ability to work with both technical and business stakeholders.
Prior experience leveraging LLMs or other GenAI tooling.
Proven ability to work across cross-functional teams.
Proven ability to work with ambiguity or when requirements are lacking/changing.
Owns a problem from raw source through pipeline, model/analysis, and production handoff.
Checks bias, leakage, sample size, and whether a metric actually answers the question.
Writes tested, versioned, observable code — not one-off notebooks that only run on a laptop.
Strong analytical and problem-solving abilities.
Excellent communication skills, with the ability to convey complex information clearly.
Proven experience in managing and delivering projects effectively.
Ability to build and manage relationships with clients and stakeholders.

Nice To Haves

A passion for delivering commercial software products and platforms to market
Deeply technical and constantly in a state of learning
Able to communicate clearly and efficiently with a variety of audiences including developers, clients, customers, partners and executives
Fearless in getting “hands-on” with technology and execution
Strong understanding of modern software engineering processes
Deep understanding and interest in cloud and distributed systems architectures
Comfortable with ambiguity with a drive for clarity
Experience leading and growing technical talent (for software engineering managers)
Experience in managing change effectively.
Experience with advanced data analytics tools and techniques.
Familiarity with machine learning concepts and applications.
Knowledge of industry trends and best practices in data engineering.

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.
Design and build AI and analytics solutions that deliver significant business value.
Collaborate with other data and analytics professionals, management, and stakeholders to ensure that technical requirements align with business needs.
Create scalable data architecture and modeling solutions that support the entire data asset lifecycle.