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About This Role
Role description AI Data Engineer
The Role
UST is searching for an AI Data Engineer with experience with libraries like Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch.
What You’ll Do
- Design experiments (A/B testing) to validate hypotheses
- Communicate findings through dashboards, reports, and visualizations
- Collaborate with cross-functional teams (engineering, product, business)
- Optimize data pipelines and workflows for scalability
- Stay updated with the latest tools and techniques in data science
This position description identifies the responsibilities and tasks typically associated with the performance of the position. Other relevant essential functions may be required.
What You Need
- 3+ Years of experience
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or related field
- Strong programming skills in Python or R
- Solid understanding of statistics and probability
- Knowledge of SQL and database systems
- Familiarity with cloud platforms (AWS, Azure, or GCP) is a plus
- Preferred Qualifications:
- Experience with big data tools (Spark, Hadoop)
- Knowledge of deep learning or NLP
- Experience in deploying models (MLOps, APIs)
- Strong problem-solving and analytical thinking
Location & Compensation
Compensation can differ depending on factors including but not limited to the specific office location, role, skill set, education, and level of experience. UST provides a reasonable range of compensation for roles that may be hired in various U.S. markets as set forth below.
Role Location: Issaquah, Washington
Compensation Range: $74,000-$111,000
Who We Are
Since 1999, UST has worked side by side with the world’s best companies to make a powerful impact through transformation. Headquartered in the United States, UST is powered by technology, driven by AI, inspired by people, and led by our purpose, we partner with our clients from design to operation. Our AI-driven digital solutions, proprietary platforms, engineering, R&D, products, and innovation ecosystem turn core challenges into impactful, disruptive business outcomes. With deep industry knowledge and a future-ready mindset, we infuse expertise, innovation, and agility into our clients’ organizations—delivering measurable value and positive lasting change for them, their customers, and communities around the world. Together, with 30,000+ employees in 30+ countries, we build for boundless impact—touching billions of lives in the process. Visit us at .
Benefits
Full-time, regular employees accrue a minimum of 10 days of paid vacation per year, receive 6 days of paid sick leave each year (pro-rated for new hires throughout the year), 10 paid holidays, and are eligible for paid bereavement leave and jury duty. They are eligible to participate in the Company’s 401(k) Retirement Plan with employer matching. They and their dependents residing in the US are eligible for medical, dental, and vision insurance, as well as the following Company-paid Employee Only benefits: basic life insurance, accidental death and disability insurance, and short- and long-term disability benefits. Regular employees may purchase additional voluntary short-term disability benefits, and participate in a Health Savings Account (HSA) as well as a Flexible Spending Account (FSA) for healthcare, dependent child care, and/or commuting expenses as allowable under IRS guidelines. Benefits offerings vary in Puerto Rico.
Part-time employees receive 6 days of paid sick leave each year (pro-rated for new hires throughout the year) and are eligible to participate in the Company’s 401(k) Retirement Plan with employer matching.
Full-time temporary employees receive 6 days of paid sick leave each year (pro-rated for new hires throughout the year) and are eligible to participate in the Company’s 401(k) program with employer matching. They and their dependents residing in the US are eligible for medical, dental, and vision insurance.
Part-time temporary employees receive 6 days of paid sick leave each year (pro-rated for new hires throughout the year).
All US employees who work in a state or locality with more generous paid sick leave benefits than specified here will receive the benefit of those sick leave laws.
Equal Employment Opportunity Statement
UST is an Equal Opportunity Employer.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other applicable characteristics protected by law. We will consider qualified applicants with arrest or conviction records in accordance with state and local laws and “fair chance” ordinances.
UST reserves the right to periodically redefine your roles and responsibilities based on the requirements of the organization and/or your performance.
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Skills
NLP, Python, Google Cloud Platform, Machine Learning, ETL, SQL, MLOps
Benefits
Compensation range: $74,000.00 to $111,000.00 per year
About UST
UST is a global digital transformation solutions provider. For more than 20 years, UST has worked side by side with the world’s best companies to make a real impact through transformation. Powered by technology, inspired by people and led by purpose, UST partners with their clients from design to operation. With deep domain expertise and a future-proof philosophy, UST embeds innovation and agility into their clients’ organizations. With over 30,000 employees in 30 countries, UST builds for boundless impact—touching billions of lives in the process.
Salary Context
This $74K-$111K 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 UST, 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 ($92K) sits 48% below the category median. Disclosed range: $74K to $111K.
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.
UST AI Hiring
UST has 14 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, Data Scientist, MLOps Engineer. Positions span Aliso Viejo, CA, US, Trivandrum, Kerala, India, Bentonville, AR. Compensation range: $90K - $123K.
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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