Head of Data Engineering

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.

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