Head of Data Jobs
Head of Data jobs are open across fintech, healthtech, e-commerce, and enterprise software, from senior manager to VP and C-suite, with specializations in data strategy, analytics engineering, and data governance. Scan the live roles below and apply to whichever ones fit.
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Texas Instruments is seeking an experienced and visionary Head of Data Engineering to lead the strategy, architecture, engineering, and delivery of enterprise data capabilities across the company.
As part of the Data & Agentic Platforms and Solutions (DAPS) organization, this leader will be responsible for building and scaling a modern, reliable, secure, and reusable data ecosystem that powers business operations, advanced analytics, AI, and agentic solutions.
The role requires a hands-on technology leader who can drive enterprise-wide data engineering transformation, modernize legacy data integration patterns, and establish engineering practices that enable trusted, near-real-time access to data across product design , testing, manufacturing, supply chain, product engineering, customer, and enterprise functions.
The successful candidate will combine deep technical expertise with strong organizational leadership, execution discipline, and the ability to translate complex data challenges into measurable business outcomes.
Responsibilities
1. Enterprise Data Engineering Strategy & Architecture
- Define and execute the enterprise data engineering strategy aligned with TI's business priorities and DAPS technology roadmap.
- Architect scalable data integration, ingestion, transformation, and delivery capabilities across on-premises, factory, and cloud environments.
- Drive modernization from traditional batch-oriented and point-to-point data movement toward event-driven, real-time, and reusable data architectures.
- Establish standards for data pipelines, data contracts, data modeling, orchestration, and engineering practices.
- Partner with enterprise architecture and platform engineering teams to drive adoption of open, interoperable data architectures.
2. Data Integration & Real-Time Engineering
- Lead the design and implementation of enterprise-scale batch, streaming, and change data capture (CDC) pipelines.
- Build robust data ingestion and integration capabilities using technologies such as Kafka, Spark, Flink, and modern data orchestration frameworks.
- Enable near-real-time data availability for critical decision-making.
- Drive reusable, metadata-driven integration patterns that reduce duplication and accelerate onboarding of new data sources.
- Ensure data pipelines meet demanding requirements for throughput, latency, reliability, and recoverability.
3. Enterprise Data Products & Data Domains
- Lead engineering delivery of trusted, reusable data products across domains.
- Partner with domain owners, business teams, and data architects to establish authoritative sources and integrated data models.
- Build standardized, governed data products accessible through APIs, events, and analytical interfaces.
- Enable end-to-end data connectivity, traceability, and integration across business processes.
- Establish engineering practices for data product lifecycle management, versioning, and service-level commitments.
4. Modern Data Platforms & Engineering
- Drive adoption of modern lakehouse architectures, open table formats, and distributed data processing for on prem air gapped systems and multi cloud hybrid systems.
- Engineer scalable data solutions using technologies such as Apache Iceberg, Spark, Trino, Databricks, and high-performance analytical databases.
- Optimize data processing for performance, scalability, cost, and operational efficiency across hybrid environments.
- Establish reusable frameworks for ingestion, transformation, data validation, and publishing.
- Partner with platform teams to ensure consistent engineering practices across enterprise data infrastructure.
5. Data Quality, Reliability & Governance
- Embed data quality, observability, lineage, and security into engineering workflows by design.
- Establish automated data validation, reconciliation, anomaly detection, and monitoring.
- Define and manage service-level objectives for data freshness, completeness, accuracy, and availability.
- Partner with the Data & AI Governance Office to implement classification, access controls, retention, and compliance requirements.
- Drive proactive incident prevention, root-cause analysis, and continuous improvement of data reliability.
6. AI-Ready Data & Knowledge Engineering
- Enable trusted, contextualized, and accessible enterprise data for AI, machine learning, and agentic applications.
- Build data engineering capabilities that support semantic layers, knowledge products, retrieval systems, and enterprise AI solutions.
- Partner with AI engineering teams to ensure reliable data access through governed APIs, structured datasets, and knowledge interfaces.
- Establish patterns for securely exposing enterprise data to agents while maintaining access controls, lineage, and accountability.
7. Engineering Leadership & Operational Excellence
- Build, lead, and develop a high-performing organization of data engineers, technical leads, and engineering managers.
- Establish strong engineering disciplines including CI/CD, DataOps, infrastructure as code, automated testing, and production observability.
- Drive engineering productivity through standardization, automation, reusable components, and developer enablement.
- Own delivery commitments, operational reliability, technical debt reduction, and continuous platform improvement within the data engineering function.
- Partner with business and technology stakeholders to prioritize investments based on measurable business impact.
- Develop technical talent, succession plans, and a culture of ownership, accountability, and engineering excellence.
Qualifications
Minimum Requirements:
- Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related technical field.
- 15+ years of experience in software engineering, data engineering, or enterprise data architecture, including significant experience leading large engineering organizations.
- Demonstrated success designing and operating enterprise-scale data platforms in complex, global environments.
- Deep expertise in distributed data processing, data integration, streaming architectures, and modern data engineering frameworks.
- Deep expertise in deploying workload on Kubernetes environments
- Strong experience with technologies such as Kafka, Spark, Flink, Python, SQL, and modern data orchestration platforms.
- Experience with lakehouse architectures, open table formats such as Apache Iceberg or Delta Lake, and distributed analytical systems.
- Strong understanding of data modeling, data quality, metadata management, governance, and security.
- Proven ability to lead large-scale technology modernization and deliver reliable, production-grade engineering solutions.
- Strong executive communication, stakeholder management, and cross-functional leadership skills
About Us
- The future starts here. As a TIer, you'll play a critical role in defining the next era of semiconductor technology and help create the innovations that shape the world around us.
- Surround yourself with a team of innovators who value your unique perspective and will challenge you to bring your best self to work every day. We believe that fostering a culture of collaboration fuels both personal growth and technological breakthroughs. Explore our company culture.
- At TI, your success goes beyond your work. Take advantage of comprehensive benefits designed to support your total well-being at work and in life, from competitive compensation to extensive health coverage and wellness programs. Learn more about our benefits offerings.
Job Info
- Job Identification 25011018
- Job Category Information Technology
- Posting Date 11/10/2026, 06:39 PM
- Degree Level Bachelor's Degree
- Locations EXKI 13560 N Central Expy, Dallas, TX, 75243, US
- ECL/GTC Required Yes
Texas Instruments is seeking an experienced and visionary Head of Data Engineering to lead the strategy, architecture, engineering, and delivery of enterprise data capabilities across the company.
As part of the Data & Agentic Platforms and Solutions (DAPS) organization, this leader will be responsible for building and scaling a modern, reliable, secure, and reusable data ecosystem that powers business operations, advanced analytics, AI, and agentic solutions.
The role requires a hands-on technology leader who can drive enterprise-wide data engineering transformation, modernize legacy data integration patterns, and establish engineering practices that enable trusted, near-real-time access to data across product design , testing, manufacturing, supply chain, product engineering, customer, and enterprise functions.
The successful candidate will combine deep technical expertise with strong organizational leadership, execution discipline, and the ability to translate complex data challenges into measurable business outcomes.
Responsibilities
1. Enterprise Data Engineering Strategy & Architecture
- Define and execute the enterprise data engineering strategy aligned with TI's business priorities and DAPS technology roadmap.
- Architect scalable data integration, ingestion, transformation, and delivery capabilities across on-premises, factory, and cloud environments.
- Drive modernization from traditional batch-oriented and point-to-point data movement toward event-driven, real-time, and reusable data architectures.
- Establish standards for data pipelines, data contracts, data modeling, orchestration, and engineering practices.
- Partner with enterprise architecture and platform engineering teams to drive adoption of open, interoperable data architectures.
2. Data Integration & Real-Time Engineering
- Lead the design and implementation of enterprise-scale batch, streaming, and change data capture (CDC) pipelines.
- Build robust data ingestion and integration capabilities using technologies such as Kafka, Spark, Flink, and modern data orchestration frameworks.
- Enable near-real-time data availability for critical decision-making.
- Drive reusable, metadata-driven integration patterns that reduce duplication and accelerate onboarding of new data sources.
- Ensure data pipelines meet demanding requirements for throughput, latency, reliability, and recoverability.
3. Enterprise Data Products & Data Domains
- Lead engineering delivery of trusted, reusable data products across domains.
- Partner with domain owners, business teams, and data architects to establish authoritative sources and integrated data models.
- Build standardized, governed data products accessible through APIs, events, and analytical interfaces.
- Enable end-to-end data connectivity, traceability, and integration across business processes.
- Establish engineering practices for data product lifecycle management, versioning, and service-level commitments.
4. Modern Data Platforms & Engineering
- Drive adoption of modern lakehouse architectures, open table formats, and distributed data processing for on prem air gapped systems and multi cloud hybrid systems.
- Engineer scalable data solutions using technologies such as Apache Iceberg, Spark, Trino, Databricks, and high-performance analytical databases.
- Optimize data processing for performance, scalability, cost, and operational efficiency across hybrid environments.
- Establish reusable frameworks for ingestion, transformation, data validation, and publishing.
- Partner with platform teams to ensure consistent engineering practices across enterprise data infrastructure.
5. Data Quality, Reliability & Governance
- Embed data quality, observability, lineage, and security into engineering workflows by design.
- Establish automated data validation, reconciliation, anomaly detection, and monitoring.
- Define and manage service-level objectives for data freshness, completeness, accuracy, and availability.
- Partner with the Data & AI Governance Office to implement classification, access controls, retention, and compliance requirements.
- Drive proactive incident prevention, root-cause analysis, and continuous improvement of data reliability.
6. AI-Ready Data & Knowledge Engineering
- Enable trusted, contextualized, and accessible enterprise data for AI, machine learning, and agentic applications.
- Build data engineering capabilities that support semantic layers, knowledge products, retrieval systems, and enterprise AI solutions.
- Partner with AI engineering teams to ensure reliable data access through governed APIs, structured datasets, and knowledge interfaces.
- Establish patterns for securely exposing enterprise data to agents while maintaining access controls, lineage, and accountability.
7. Engineering Leadership & Operational Excellence
- Build, lead, and develop a high-performing organization of data engineers, technical leads, and engineering managers.
- Establish strong engineering disciplines including CI/CD, DataOps, infrastructure as code, automated testing, and production observability.
- Drive engineering productivity through standardization, automation, reusable components, and developer enablement.
- Own delivery commitments, operational reliability, technical debt reduction, and continuous platform improvement within the data engineering function.
- Partner with business and technology stakeholders to prioritize investments based on measurable business impact.
- Develop technical talent, succession plans, and a culture of ownership, accountability, and engineering excellence.
Qualifications
Minimum Requirements:
- Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related technical field.
- 15+ years of experience in software engineering, data engineering, or enterprise data architecture, including significant experience leading large engineering organizations.
- Demonstrated success designing and operating enterprise-scale data platforms in complex, global environments.
- Deep expertise in distributed data processing, data integration, streaming architectures, and modern data engineering frameworks.
- Deep expertise in deploying workload on Kubernetes environments
- Strong experience with technologies such as Kafka, Spark, Flink, Python, SQL, and modern data orchestration platforms.
- Experience with lakehouse architectures, open table formats such as Apache Iceberg or Delta Lake, and distributed analytical systems.
- Strong understanding of data modeling, data quality, metadata management, governance, and security.
- Proven ability to lead large-scale technology modernization and deliver reliable, production-grade engineering solutions.
- Strong executive communication, stakeholder management, and cross-functional leadership skills
About Us
- The future starts here. As a TIer, you'll play a critical role in defining the next era of semiconductor technology and help create the innovations that shape the world around us.
- Surround yourself with a team of innovators who value your unique perspective and will challenge you to bring your best self to work every day. We believe that fostering a culture of collaboration fuels both personal growth and technological breakthroughs. Explore our company culture.
- At TI, your success goes beyond your work. Take advantage of comprehensive benefits designed to support your total well-being at work and in life, from competitive compensation to extensive health coverage and wellness programs. Learn more about our benefits offerings.
Job Info
- Job Identification 25011018
- Job Category Information Technology
- Posting Date 11/10/2026, 06:39 PM
- Degree Level Bachelor's Degree
- Locations EXKI 13560 N Central Expy, Dallas, TX, 75243, US
- ECL/GTC Required Yes
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Who's Hiring
Top Industries Hiring
- Banking & Financial Services
- Food & Beverage
- Accounting & Auditing
- Technology & Software
- Healthcare & Medical Services
What Employers Look For
The qualifications that appear most often in head of data jobs.
- 7 or more years of experience in data analytics, data engineering, or business intelligence
- Proven experience managing and growing a data team of analysts, engineers, or scientists
- Advanced SQL and proficiency with at least one major cloud data platform such as Snowflake, BigQuery, or Databricks
- Demonstrated ability to build and execute a data strategy aligned to company-wide business goals
- Experience with data governance frameworks, data quality standards, and privacy compliance
- Bachelor's or master's degree in a quantitative field such as computer science, statistics, or mathematics
Tips for Your Head of Data Job Search
Quantify your data team's business impact
Hiring panels for head of data roles want outcomes, not responsibilities. Rewrite your resume bullets to show revenue influenced, cost reduced, or decisions accelerated by work your team shipped, not just the size of pipelines you built.
Demonstrate cross-functional influence on paper
Head of data candidates who stall in screening often look too technical. Add a section or summary line that names the product, finance, or operations stakeholders you partnered with and what those partnerships delivered for the business.
Apply early to roles that fit
Migrate Mate lists head of data openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Filter postings by data maturity stage
A startup building its first data stack needs a different head of data than a scaled company optimizing an existing platform. Read job descriptions for clues like 'greenfield,' 'modernize,' or 'scale,' and tailor your cover letter to that exact stage.
Prepare a data organization case study
Late-stage interviews almost always include a whiteboard or take-home asking how you would structure a data org. Have a ready example from your history where you hired, restructured, or federated a team, with the rationale and what you learned.
Negotiate scope before you negotiate compensation
Before accepting or countering an offer, clarify reporting lines, budget authority, and whether the role owns machine learning or only analytics. Misaligned scope is the most common reason newly hired heads of data leave within 18 months.
Head of Data Jobs: Frequently Asked Questions
Which companies are hiring the most head of datas?
The most active employers for head of datas right now are Gusto, 6sense, and OpenAI, and the most openings are in California, Massachusetts, and New York, based on current listings on Migrate Mate as of November 2026. Demand is especially high in fintech, healthtech, and enterprise SaaS companies scaling their data platforms.
How many head of data jobs are remote?
About 75% of head of data openings are fully remote or hybrid as of November 2026, reflecting strong demand for senior data leadership that can operate across distributed teams. Roles focused on data strategy and analytics leadership tend to be more remote-friendly than those tied to real-time infrastructure or on-site stakeholder relationships.
How do you become a head of data?
Start by building deep hands-on expertise in data engineering, analytics, or data science, then move into a team lead or manager role to develop people management skills. From there, take on cross-functional projects that expose you to business strategy. Most heads of data progress through senior IC roles before stepping into director or VP positions, so demonstrating both technical fluency and organizational leadership is essential.
Can you get hired as a head of data without prior leadership experience?
It's rare at a large company, but startups frequently hire strong senior individual contributors into their first head of data role if they can articulate a clear vision for building a data function from scratch. Strengthen your case by leading a cross-functional data initiative, mentoring junior teammates, and presenting a concrete org-build plan during your interview that shows you've thought through hiring, tooling, and governance.
What does the head of data interview process look like?
Most processes run three to five rounds covering a recruiter screen, a hiring manager conversation focused on your data leadership philosophy, a technical deep-dive on architecture and tooling decisions, a stakeholder or executive panel assessing your ability to influence without authority, and a take-home or whiteboard case on how you would build or restructure a data organization. References and a final offer conversation typically follow.
Where can I find and apply to head of data jobs?
You can find and apply to head of data jobs on Migrate Mate, which lists current openings from companies across the United States. Search the roles available, find the ones that match your experience and goals, and apply directly to each listing that fits.
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Find roles that match your experience and apply in just a few clicks.
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