AI Data Engineer Jobs in Missouri
AI Data Engineer jobs in Missouri are in steady demand, with the strongest hiring concentrated in financial services, healthcare, and logistics technology across Kansas City and St. Louis. Large, established employers including Cerner (Oracle Health), Edward Jones, and Centene Corporation have built meaningful data engineering teams in the state and continue to hire at mid-level and senior levels. Pipeline architecture, MLOps, and cloud data platform work are the most sought-after specialties across Missouri listings. See the openings below and apply to the ones that match your experience.
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Showing 5 of 14+ AI Data Engineer jobs
Description
The Director, Operating Model & Strategy owns and drives the Enterprise Data & AI roadmap and the operating model that executes it. Reporting to the Managing Director, Data & AI, this leader ensures business engagement, engineering and AI delivery, portfolio execution, governance, capacity, and performance work as one cohesive function. The Department Manager is accountable for tracking and reporting portfolio value, delivery, and P&L impact, and for turning strategy into a disciplined, measure able execution system.
Key Responsibilities
Strategy & Roadmap
- Own and continuously evolve the Enterprise Data & AI Strategy and 1–3 year roadmap, aligned to business priorities and the Business Enablement strategy.
- Translate strategy into annual operating priorities, OKRs, KPIs, initiatives, investment requirements, and accountable owners.
Operating Model & Execution
- Own and run the Data & AI operating model and its cadences, ensuring clear communication, coordination, decision-making, escalation, and execution across teams.
- Lead the Business Analysis, Scrum/Agile, and Program/Portfolio Management capabilities and deploy them to priority work.
- Drive continuous improvement so operating mechanisms become simpler, faster, more transparent, and scalable as the function matures.
- Hire, develop, and retain top talent across Business Analysis, Scrum/Agile, and Program/Portfolio Management, build enduring capability, and lead teams across multiple locations.
Portfolio, Value & Performance
- Own the Shared Business Backlog and portfolio process: prioritization, sequencing, dependencies, milestones, risk, and an integrated capacity view.
- Accountable for tracking portfolio value, delivery, and P&L impact; establish a consistent performance system covering value, delivery, adoption, capacity, risk, and operating health.
- Own Data & AI executive reporting, including value validation in partnership with Finance.
Governance
- Operate the Data & AI Operating Committee (DOC) and Data Executive Operating Committee (DEOC) routines so forums receive decision-quality information and resolve trade-offs at the right level.
- Orchestrate AI governance across Business Enablement Strategy, Data & AI, IT, Architecture, Security, Legal, Risk, and business stakeholders, maintaining clear decision rights, workflows, and control visibility.
Partnerships & Resources
- Partner with the Director, Business Data Engagement so qualified demand, sponsor commitments, and adoption requirements are reflected in portfolio decisions.
- Partner with the Director, Data Engineering & AI so priorities are technically feasible, capacity-backed, sequenced, and delivered to common standards.
- Coordinate workforce, vendor/SI, budget, and capacity planning with the Managing Director and enterprise partners; surface friction, constraints, unclear ownership, and delivery risks early and drive resolution.
Stakeholder Influence
- Influence executives and governance forums to make timely, evidence-based trade-off and investment decisions.
- Align the three Data & AI leadership pillars and enterprise partners (IT, Security, Legal, Risk, Finance) through influence rather than positional authority.
- Hold the function accountable to its commitments (priorities, milestones, value) and make delivery performance visible to leadership.
- Build a high-trust culture where escalation, constructive challenge, and course correction are routine.
What Success Looks Like (First 12 Months)
Roadmap: Enterprise Data & AI Strategy and 1–3 year roadmap approved by the Managing Director and executive sponsors, with annual OKRs and KPIs cascaded to named owners.
Portfolio: A single Shared Business Backlog and integrated capacity view in use, with prioritization decisions made through the portfolio process.
Reporting: A baseline performance dashboard reviewed monthly with the Managing Director, covering value, delivery, adoption, capacity, risk, and operating health.
Value: Value and P&L impact tracked for priority initiatives and validated with Finance, with a clear line from investment to realized benefit.
Governance: DOC/DEOC operating on a predictable cadence; decisions and escalations resolved on time with documented decision rights.
Delivery: On-time delivery against committed milestones, with cross-team risks and capacity conflicts identified early. Targets to be set with the Managing Director at the start of the role.
Qualifications
- Bachelor’s degree in business, engineering, computer science, or a related field from an accredited program, and a minimum of 15 years of related professional experience across enterprise strategy execution, operating models, portfolio/program management, data/technology transformation, or business operations, including significant leadership responsibility.
- Demonstrated experience running complex cross-functional operations and converting enterprise strategy into roadmaps, portfolios, operating cadences, and measurable execution.
- Experience leading program/portfolio management, business analysis, agile/Scrum, or comparable delivery-management capabilities.
- Proven ability to hire and develop top talent and to lead teams across multiple locations.
- Strong understanding of Data & AI delivery lifecycles and how business demand, engineering capacity, governance, adoption, and value realization connect.
- Experience establishing enterprise OKRs/KPIs, executive reporting, portfolio health measures, and risk/dependency management.
- Strong executive communication, facilitation, and influence skills in a matrixed environment, with the ability to create clarity from ambiguity and build accountability without unnecessary bureaucracy.
- Strong business and financial acumen, including investment prioritization, workforce/capacity planning, vendor management, and value realization, with a track record of measurable business impact from data.
- Experience in engineering, construction, EPC, professional services, or another federated, project-centric enterprise preferred.
- Experience establishing or operating enterprise Data & AI strategy, governance, and operating models preferred.
- Experience with AI governance, responsible AI frameworks, enterprise technology governance, or cross-functional risk/control models preferred.
- Experience partnering with Finance on investment governance and benefits/value validation preferred.
- Experience leading transformation across autonomous business units while preserving local agility and enterprise standards preferred.
This job posting will remain open a minimum of 72 hours and on an ongoing basis until filled.
EEO/Disabled/Veterans
Job Information Technology
Primary Location US-MO-Kansas City
Other Locations US-TX-Houston, US-TX-Dallas
Schedule: Full-time
Travel: Yes, 5 % of the Time
Req ID: 265230
Job Hire Type Experienced #LI-GS #COR N/A
Description
The Director, Operating Model & Strategy owns and drives the Enterprise Data & AI roadmap and the operating model that executes it. Reporting to the Managing Director, Data & AI, this leader ensures business engagement, engineering and AI delivery, portfolio execution, governance, capacity, and performance work as one cohesive function. The Department Manager is accountable for tracking and reporting portfolio value, delivery, and P&L impact, and for turning strategy into a disciplined, measure able execution system.
Key Responsibilities
Strategy & Roadmap
- Own and continuously evolve the Enterprise Data & AI Strategy and 1–3 year roadmap, aligned to business priorities and the Business Enablement strategy.
- Translate strategy into annual operating priorities, OKRs, KPIs, initiatives, investment requirements, and accountable owners.
Operating Model & Execution
- Own and run the Data & AI operating model and its cadences, ensuring clear communication, coordination, decision-making, escalation, and execution across teams.
- Lead the Business Analysis, Scrum/Agile, and Program/Portfolio Management capabilities and deploy them to priority work.
- Drive continuous improvement so operating mechanisms become simpler, faster, more transparent, and scalable as the function matures.
- Hire, develop, and retain top talent across Business Analysis, Scrum/Agile, and Program/Portfolio Management, build enduring capability, and lead teams across multiple locations.
Portfolio, Value & Performance
- Own the Shared Business Backlog and portfolio process: prioritization, sequencing, dependencies, milestones, risk, and an integrated capacity view.
- Accountable for tracking portfolio value, delivery, and P&L impact; establish a consistent performance system covering value, delivery, adoption, capacity, risk, and operating health.
- Own Data & AI executive reporting, including value validation in partnership with Finance.
Governance
- Operate the Data & AI Operating Committee (DOC) and Data Executive Operating Committee (DEOC) routines so forums receive decision-quality information and resolve trade-offs at the right level.
- Orchestrate AI governance across Business Enablement Strategy, Data & AI, IT, Architecture, Security, Legal, Risk, and business stakeholders, maintaining clear decision rights, workflows, and control visibility.
Partnerships & Resources
- Partner with the Director, Business Data Engagement so qualified demand, sponsor commitments, and adoption requirements are reflected in portfolio decisions.
- Partner with the Director, Data Engineering & AI so priorities are technically feasible, capacity-backed, sequenced, and delivered to common standards.
- Coordinate workforce, vendor/SI, budget, and capacity planning with the Managing Director and enterprise partners; surface friction, constraints, unclear ownership, and delivery risks early and drive resolution.
Stakeholder Influence
- Influence executives and governance forums to make timely, evidence-based trade-off and investment decisions.
- Align the three Data & AI leadership pillars and enterprise partners (IT, Security, Legal, Risk, Finance) through influence rather than positional authority.
- Hold the function accountable to its commitments (priorities, milestones, value) and make delivery performance visible to leadership.
- Build a high-trust culture where escalation, constructive challenge, and course correction are routine.
What Success Looks Like (First 12 Months)
Roadmap: Enterprise Data & AI Strategy and 1–3 year roadmap approved by the Managing Director and executive sponsors, with annual OKRs and KPIs cascaded to named owners.
Portfolio: A single Shared Business Backlog and integrated capacity view in use, with prioritization decisions made through the portfolio process.
Reporting: A baseline performance dashboard reviewed monthly with the Managing Director, covering value, delivery, adoption, capacity, risk, and operating health.
Value: Value and P&L impact tracked for priority initiatives and validated with Finance, with a clear line from investment to realized benefit.
Governance: DOC/DEOC operating on a predictable cadence; decisions and escalations resolved on time with documented decision rights.
Delivery: On-time delivery against committed milestones, with cross-team risks and capacity conflicts identified early. Targets to be set with the Managing Director at the start of the role.
Qualifications
- Bachelor’s degree in business, engineering, computer science, or a related field from an accredited program, and a minimum of 15 years of related professional experience across enterprise strategy execution, operating models, portfolio/program management, data/technology transformation, or business operations, including significant leadership responsibility.
- Demonstrated experience running complex cross-functional operations and converting enterprise strategy into roadmaps, portfolios, operating cadences, and measurable execution.
- Experience leading program/portfolio management, business analysis, agile/Scrum, or comparable delivery-management capabilities.
- Proven ability to hire and develop top talent and to lead teams across multiple locations.
- Strong understanding of Data & AI delivery lifecycles and how business demand, engineering capacity, governance, adoption, and value realization connect.
- Experience establishing enterprise OKRs/KPIs, executive reporting, portfolio health measures, and risk/dependency management.
- Strong executive communication, facilitation, and influence skills in a matrixed environment, with the ability to create clarity from ambiguity and build accountability without unnecessary bureaucracy.
- Strong business and financial acumen, including investment prioritization, workforce/capacity planning, vendor management, and value realization, with a track record of measurable business impact from data.
- Experience in engineering, construction, EPC, professional services, or another federated, project-centric enterprise preferred.
- Experience establishing or operating enterprise Data & AI strategy, governance, and operating models preferred.
- Experience with AI governance, responsible AI frameworks, enterprise technology governance, or cross-functional risk/control models preferred.
- Experience partnering with Finance on investment governance and benefits/value validation preferred.
- Experience leading transformation across autonomous business units while preserving local agility and enterprise standards preferred.
This job posting will remain open a minimum of 72 hours and on an ongoing basis until filled.
EEO/Disabled/Veterans
Job Information Technology
Primary Location US-MO-Kansas City
Other Locations US-TX-Houston, US-TX-Dallas
Schedule: Full-time
Travel: Yes, 5 % of the Time
Req ID: 265230
Job Hire Type Experienced #LI-GS #COR N/A
See All 14 AI Data Engineer Jobs in Missouri
Find roles in Missouri that match your experience and apply in just a few clicks.
Find AI Data Engineer JobsAI Data Engineer Jobs by City in Missouri
Where Missouri roles are concentrated, by current openings.
AI Data Engineer Job Market in Missouri
A snapshot from current Missouri openings, updated as new roles post.
Who's Hiring
What Missouri Employers Look For
The qualifications that appear most often in AI data engineer jobs across Missouri.
- Bachelor's or master's degree in computer science, data engineering, or a related technical field
- Hands-on experience building and maintaining data pipelines using Apache Spark, Kafka, or Airflow
- Proficiency with cloud data platforms such as AWS, Azure, or Google Cloud in production environments
- Strong SQL skills combined with experience in Python or Scala for large-scale data transformation
- Experience integrating machine learning models or AI workloads into enterprise data infrastructure
- Familiarity with data governance, cataloging tools, and modern lakehouse or warehouse architectures
AI Data Engineer Jobs in Missouri: Frequently Asked Questions
How do you become a ai data engineer in Missouri?
Missouri does not require a state-issued license to work as an ai data engineer. The standard hiring path begins with a bachelor's degree in computer science, data science, or software engineering, followed by hands-on experience with cloud platforms and pipeline tools. Missouri employers, particularly in the Kansas City and St. Louis technology corridors, consistently favor candidates who hold cloud certifications from AWS, Google, or Microsoft alongside a portfolio of deployed data projects.
How much do AI data engineers make in Missouri?
AI data engineers in Missouri earn a median of about $97,090 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $58,960 for the lowest 10% to over $151,380 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire ai data engineers in Missouri?
Companies currently hiring ai data engineers in Missouri include ATB Technologies, Burns & McDonnell, and Deloitte, per current listings on Migrate Mate as of November 2026. Missouri's concentration of healthcare IT, financial services, and insurance firms means demand for ai data engineers remains relatively consistent throughout the year rather than spiking seasonally.
Which Missouri cities have the most ai data engineer jobs?
Kansas City, St. Louis, and Chesterfield account for the largest share of ai data engineer openings in Missouri. Kansas City leads because of its dense cluster of financial services, healthcare technology, and logistics firms, while St. Louis draws demand from large insurance carriers, biomedical research institutions, and enterprise software companies headquartered in the region.
Are there remote ai data engineer jobs in Missouri?
Yes, and more than most fields. About 75% of ai data engineer openings tied to Missouri are remote or hybrid as of November 2026, reflecting how naturally this work translates to distributed settings. Pipeline development, model integration, and cloud infrastructure work are the parts of the role most commonly offered fully remote, while on-site expectations tend to concentrate around stakeholder alignment and data governance responsibilities.
How can I get hired as a ai data engineer in Missouri with little or no experience?
The most realistic entry path is moving laterally from a data analyst or software engineering role into ai data engineering by building cloud and pipeline skills on the side. Missouri's large healthcare and financial services employers, including hospital networks and insurance carriers in Kansas City and St. Louis, occasionally hire associate or junior data engineers through rotational technology programs aimed at recent graduates. Earning a cloud data certification and completing a public portfolio project involving pipeline orchestration or model deployment gives Missouri hiring managers a concrete reason to take a chance on a candidate without direct experience.
Where can I find and apply to ai data engineer jobs in Missouri?
You can find and apply to ai data engineer jobs in Missouri on Migrate Mate, which lists current Missouri openings updated regularly. Search the listings, find the roles that match your background and target city, and apply directly to the ones that fit.
See All 14 AI Data Engineer Jobs in Missouri
Find roles in Missouri that match your experience and apply in just a few clicks.
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