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About This Role
Location: McLean Other locations: Primary Location Only Salary: Competitive Date: Oct 5, 2026 Job description
Requisition ID: 1749608 At EY, we’re all in to shape your future with confidence.
We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.
Government and Infrastructure – Technology Consulting - AI & Data - Data Engineer – Top Secret Clearance
From strategy to execution, the Government and Infrastructure practice of Ernst & Young provides a full range of consulting and audit services to help our Federal, State, Local and Education clients implement new ideas to help achieve their mission outcomes. We deliver real change and measurable results through our diverse, high-performing teams, quality work at the highest professional standards, operational know-how from across our global organization, and creative and bold ideas that drive innovation. We enable our government clients to achieve their mission of protecting the nation and serving the people; increasing public safety; improving healthcare for our military, veterans, and citizens; delivering essential public services; and helping those in need. EY is ready to help our government build a better working world.
EY delivers unparalleled service in big data, business intelligence, and digital analytics built on a blend of custom-developed methods related to customer analytics, data visualization, and optimization. We leverage best practices and a high degree of business acumen that has been compiled over years of experience to ensure the highest level of execution and satisfaction for our clients. At EY, our methods are not tied to any specific platforms but rather arrived at by analyzing business needs and making sure that the solutions delivered meet all client goals.
The opportunity You will help our clients navigate the complex world of modern AI, data and analytics. We’ll look to you to provide our clients with a unique business perspective on how data science and analytics can transform and improve their entire organization – starting with key business issues they face. This is a high growth, high visibility area with plenty of opportunities to enhance your skillset and build your career.
Your key responsibilities This role combines traditional business intelligence and reporting responsibilities with hands-on data integration, transformation, and data management activities. The successful candidate will work with stakeholders, analysts, and technical teams to design data solutions, build analytical products, and improve the accessibility, quality, and usability of enterprise data. Responsibilities blend analytical thinking, technical expertise, and client-facing consulting skills. You’ll be responsible for data analytics & business intelligence; Analyzing large datasets to identify trends, anomalies, and actionable insights.
Skills and attributes for success
- Experience developing dashboards, scorecards, and reports using Power BI
- Experience conducting ad hoc analyses supporting audits, operational assessments, and leadership inquiries. Data Engineering & Data Integration Design and maintain data extraction, transformation, and loading (ETL/ELT) processes
- History developing and optimizing SQL queries, views, and stored procedures; Integrate data from multiple enterprise systems and data sources
- Support of cloud-based data platforms and analytics environments
- Troubleshooting experience with data pipeline, data quality, and reporting issues
- Experience writing tests, participating in code reviews
- Seeking out information to learn about emerging methodologies and technologies
- Clarifying problems by driving to understand the true issue
- Experience collaborating, influencing, and building consensus through constructive relationships and effective listening
- Solving problems by incorporating data into decision making
To qualify for the role, you must have
- Must have a Bachelor's degree
- Must have an active Top Secret clearance
- 1-3 years of related experience with SQL Power BI Data modeling Data integration and transformation Data visualization Experience working with relational databases and large datasets
- Must be comfortable working in-person in the Washington, DC area
Ideally, you’ll also have
- Bachelor’s Degree or above in mathematics, information systems, statistics, computer science, or related disciplines
- Data Engineering Experience building ETL/ELT pipelines.
- Experience with Azure Data Factory, or similar technologies.
- Experience with Python for data processing and automation.
- Experience optimizing database and reporting performance. Microsoft Data Platform Azure SQL Azure Data Factory Statistical analysis and trend analysis.
- Data quality management. Dashboard development and KPI reporting. Self-service analytics solutions
Due to the nature of our work in the Government and Public Sector, work may be required to be completed at client, EY and/or contractor sites. Our goal is to assign professionals to projects within a commutable distance of their work location office. In certain circumstances, travel may be required beyond your work location based on client and project needs. Candidates should be willing to travel 20 – 30% or more.
What we offer you
At EY, we harness our collective strength to empower you to shape your future with confidence through professional growth, personal fulfillment and an inclusive culture. Learn more at ey.com/us/careers.
- We offer a comprehensive compensation and benefits package where you’ll be rewarded based on your performance and recognized for the value you bring to the business. The base salary range for this job is:
- + New York City, Boston, and Washington DC Metro Areas, Washington State, and Southern California offices – $91,400 to $136,900 + Bay Area California offices – $95,100 to $142,600 + All other offices locations in the US, including Sacramento – $76,200 to $125,500
- Individual salaries within these ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography. In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options.
- Under our flexible vacation policy, you’ll decide how much vacation time you need based on your own personal circumstances. You’ll also be granted time off for designated EY Paid Holidays, Winter/Summer breaks, Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well-being.
Are you ready to shape your future with confidence? Apply today.
- To make the most of your application experience, please limit yourself to two applications within a six-month period.
- EY accepts applications for this position on an on-going basis.
- For those living in California, please click here for additional information.
- At EY, our values set the foundation for how we work and the behaviors we expect of our people. Any misrepresentation or falsification of information or lack of integrity at any point in the recruiting process may result in withdrawal of your candidacy, revocation of an offer or immediate termination of employment.
EY | Building a better working world
EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.
Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.
EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.
All in to shape the future with confidence.
EY provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, genetic information, national origin, protected veteran status, disability status, or any other legally protected basis, including arrest and conviction records, in accordance with applicable law.
EY is committed to providing reasonable accommodation to qualified individuals with disabilities including veterans with disabilities. If you have a disability and either need assistance applying online or need to request an accommodation during any part of the application process, please call 1-800-EY-HELP3, select Option 2 for candidate related inquiries, then select Option 1 for candidate queries and finally select Option 2 for candidates with an inquiry which will route you to EY’s Talent Shared Services Team (TSS) or email the TSS at [email protected].
Salary Context
This $91K-$142K range is in the lower quartile for Data Engineer roles in our dataset (median: $140K across 33 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 9,079 AI roles we're tracking, Data Engineer positions make up 1% of the market. At EY, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $177,000 based on 180 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($117K) sits 34% below the category median. Disclosed range: $91K to $142K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
EY AI Hiring
EY has 70 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Architect, AI Safety. Positions span Stamford, CT, Hoboken, NJ, New Brunswick, NJ. Compensation range: $142K - $374K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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