AI Data Engineer Jobs
AI Data Engineer jobs are open across technology, healthcare, financial services, and retail, from entry-level to staff and principal, with specializations in machine learning pipelines, feature stores, and real-time data infrastructure. Find a role that fits from the openings below and apply directly.
Find AI Data Engineer JobsOverview
Showing 5 of 5,262+ AI Data Engineer jobs
Location: Onsite 3 days a wk; Hybrid – Office location is Chandler AZ
Required Pay Scale: $60-65hr W2
MUST BE ABLE TO ATTEND ONSITE INTERVIEW
Due to client requirements this role is only open to USC or GC candidates
Qualifications
- Strong expertise in data integration, transformation, and pipeline development
- Deep experience in data quality frameworks, validation, and monitoring
- Advanced knowledge of data modeling and metadata management
- Experience implementing data governance, lineage, and control frameworks
- Strong understanding of enterprise-scale data processing and optimization
- Solid foundation in supporting AI/ML data requirements and analytics use cases
- Bachelor’s degree in Computer Science, Data Engineering, Information Systems, mathematics, Statistics, or related field.
Required Skills
- 5+ years of experience in data engineering with strong focus on data quality and enterprise data platforms.
- Experience enabling AI-ready and analytics-driven data ecosystems
- Proven ability to implement advanced data quality and governance practices
- Strong understanding of integrated data modeling, metadata, and lifecycle management
- Experience designing hybrid data architectures (on-prem and cloud)
- Advanced proficiency in SQL and Python.
- Background in regulated environments with audit and compliance requirements
- Combines expertise in data integration, quality, modeling, and governance to deliver scalable, controlled, and fully traceable data platforms that enable AI-ready and reliable analytics outcomes.
Desired Skills
- Experience operationalizing data stewardship and control processes
- Familiarity with automated data pipeline testing, deployment, and monitoring practices
- Master's degree in a technical or data-related field is a plus
About Matlen Silver
Experience Matters. Let your experience be driven by our experience. For more than 40 years, Matlen Silver has delivered solutions for complex talent and technology needs to Fortune 500 companies and industry leaders. Led by hard work, honesty, and a trusted team of experts, we can say that Matlen Silver technology has created a solutions experience and legacy of success that is the difference in the way the world works.
Matlen Silver is an Equal Opportunity Employer and considers all applicants for all positions without regard to race, color, religion, gender, national origin, age, sexual orientation, veteran status, the presence of a non-job-related medical condition or disability, or any other legally protected status.
If you are a person with a disability needing assistance with the application or at any point in the hiring process, please contact us at email and/or phone at info@matlensilver.com // 908-393-8600
Location: Onsite 3 days a wk; Hybrid – Office location is Chandler AZ
Required Pay Scale: $60-65hr W2
MUST BE ABLE TO ATTEND ONSITE INTERVIEW
Due to client requirements this role is only open to USC or GC candidates
Qualifications
- Strong expertise in data integration, transformation, and pipeline development
- Deep experience in data quality frameworks, validation, and monitoring
- Advanced knowledge of data modeling and metadata management
- Experience implementing data governance, lineage, and control frameworks
- Strong understanding of enterprise-scale data processing and optimization
- Solid foundation in supporting AI/ML data requirements and analytics use cases
- Bachelor’s degree in Computer Science, Data Engineering, Information Systems, mathematics, Statistics, or related field.
Required Skills
- 5+ years of experience in data engineering with strong focus on data quality and enterprise data platforms.
- Experience enabling AI-ready and analytics-driven data ecosystems
- Proven ability to implement advanced data quality and governance practices
- Strong understanding of integrated data modeling, metadata, and lifecycle management
- Experience designing hybrid data architectures (on-prem and cloud)
- Advanced proficiency in SQL and Python.
- Background in regulated environments with audit and compliance requirements
- Combines expertise in data integration, quality, modeling, and governance to deliver scalable, controlled, and fully traceable data platforms that enable AI-ready and reliable analytics outcomes.
Desired Skills
- Experience operationalizing data stewardship and control processes
- Familiarity with automated data pipeline testing, deployment, and monitoring practices
- Master's degree in a technical or data-related field is a plus
About Matlen Silver
Experience Matters. Let your experience be driven by our experience. For more than 40 years, Matlen Silver has delivered solutions for complex talent and technology needs to Fortune 500 companies and industry leaders. Led by hard work, honesty, and a trusted team of experts, we can say that Matlen Silver technology has created a solutions experience and legacy of success that is the difference in the way the world works.
Matlen Silver is an Equal Opportunity Employer and considers all applicants for all positions without regard to race, color, religion, gender, national origin, age, sexual orientation, veteran status, the presence of a non-job-related medical condition or disability, or any other legally protected status.
If you are a person with a disability needing assistance with the application or at any point in the hiring process, please contact us at email and/or phone at info@matlensilver.com // 908-393-8600
See All 5,262+ AI Data Engineer Jobs
Jump back to the full list of openings and apply to any AI data engineer role that fits.
Find AI Data Engineer JobsAI Data Engineer Job Market
A snapshot from current openings nationwide, updated as new roles post.
Who's Hiring
- Apple304
- Amazon241
- Capital One134
- NVIDIA134
- TikTok109
Top Industries Hiring
- Technology & Software2,083
- Electronics & Hardware478
- Consulting & Professional Services421
- Banking & Financial Services322
- Artificial Intelligence284
What Employers Look For
The qualifications that appear most often in AI data engineer jobs.
- Proficiency in Python and SQL for data pipeline development and transformation
- Experience building and maintaining ML feature pipelines or data platforms at scale
- Hands-on work with orchestration tools such as Apache Airflow, Prefect, or Dagster
- Familiarity with cloud data platforms including AWS, GCP, or Azure data services
- Knowledge of streaming frameworks such as Apache Kafka or Apache Flink
- Bachelor's degree in computer science, data engineering, or a related technical field
Tips for Your AI Data Engineer Job Search
Tailor your resume to pipeline depth
Hiring managers for ai data engineer roles want to see end-to-end ownership, not just tool lists. Show exactly which stages of a data pipeline you designed, the scale it ran at, and the business outcome it supported.
Highlight ML pipeline tooling explicitly
Generic 'data engineering' resumes get filtered out before a human reads them. Call out specific orchestration tools like Airflow or Prefect, feature stores like Feast, and model-serving infrastructure so your resume clears automated screening.
Target roles by data stack, not just title
AI data engineer job descriptions vary widely. Filter openings by the specific stack you know best, whether that's Spark and Databricks, dbt and Snowflake, or Kafka and Flink, so you apply where your experience maps cleanest.
Apply early to roles that fit
Migrate Mate lists ai data engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prep for system design at the data layer
Most ai data engineer interviews include a system design round focused on designing scalable feature pipelines or real-time ingestion systems. Practice scoping data freshness requirements, partitioning strategies, and failure-recovery patterns before your interview.
Negotiate with infrastructure cost data
When you reach the offer stage, frame your value around measurable infrastructure outcomes you have delivered, such as reduced query latency or lower cloud compute costs. Concrete cost or reliability numbers strengthen your position more than general experience claims.
AI Data Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most ai data engineers?
The companies hiring the most ai data engineers right now include Apple, Amazon, and Capital One, with the largest share of openings in California, New York, and Washington, based on current listings on Migrate Mate as of June 2026. Demand is concentrated in technology, financial services, and healthcare organizations investing in production machine learning systems.
How many ai data engineer jobs are remote?
About 29% of ai data engineer openings are fully remote or hybrid as of June 2026, reflecting strong remote adoption across data infrastructure roles. Sub-areas focused on cloud-native pipeline development and ML platform engineering tend to have the highest share of fully remote positions, since the work is tool-driven and asynchronous by nature.
How do you become an ai data engineer?
Start by building strong fundamentals in Python, SQL, and distributed data systems, then move into hands-on work with orchestration tools and cloud data platforms. Contributing to open-source data projects or building a portfolio of end-to-end ML pipelines demonstrates practical ability. Many practitioners transition from data engineering or software engineering roles by taking on ML infrastructure work within their current team before moving into a dedicated ai data engineer position.
Can you get an ai data engineer job with little experience?
Entry-level ai data engineer roles exist, but they usually require demonstrated pipeline-building ability even without years of professional experience. Building and publishing at least one project that ingests real data, runs transformations, and feeds a model endpoint does more for your candidacy than certifications alone. Applying to companies actively expanding their ML platform teams increases your chances, since those teams often hire candidates who show strong fundamentals and can grow into the role.
What does the ai data engineer interview process look like?
Most ai data engineer interviews include a recruiter screen, a technical phone interview covering Python and SQL, a system design round focused on designing data or feature pipelines at scale, and a final loop with engineering and data science stakeholders. Some companies add a take-home component asking you to build or debug a pipeline. Behavioral questions typically probe cross-functional collaboration with data scientists and ML engineers.
Where can I find and apply to ai data engineer jobs?
You can find and apply to ai data engineer jobs on Migrate Mate, which lists current openings from across the United States. Find roles that match your experience and specialization, then apply directly to each listing from the page.
See All 5,262+ AI Data Engineer Jobs
Jump back to the full list of openings and apply to any AI data engineer role that fits.
Find AI Data Engineer Jobs