Head of Data Engineering

Texas Instruments•Dallas, TX

About The Position

Texas Instruments is seeking an experienced, hands-on technology leader 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, the role will build and scale a modern, reliable, secure, and reusable data ecosystem supporting business operations, advanced analytics, AI, and agentic solutions. The leader will drive enterprise-wide data engineering transformation, modernize legacy data integration, and establish engineering practices for trusted, near-real-time data access across product design, testing, manufacturing, supply chain, product engineering, customer, and enterprise functions. The role combines deep technical expertise with organizational leadership and execution discipline to translate complex data challenges into measurable business outcomes. It leads data engineering strategy and architecture; integration and real-time engineering; delivery of data products across domains; modern data platforms; data quality, reliability, and governance; AI-ready data and knowledge engineering; and engineering leadership and operational excellence. The position builds and develops a high-performing organization of data engineers, technical leads, and engineering managers; establishes strong engineering disciplines; owns delivery commitments and operational reliability; and partners with business and technology stakeholders to prioritize investments based on measurable business impact.

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 deploying workloads in Kubernetes environments.
Strong experience with 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.

Responsibilities

Define and execute enterprise data engineering strategy aligned with TI business priorities and the DAPS technology roadmap.
Architect scalable data integration, ingestion, transformation, and delivery capabilities across on-premises, factory, and cloud environments.
Modernize traditional batch-oriented and point-to-point data movement toward event-driven, real-time, reusable data architectures.
Establish standards for data pipelines, data contracts, data modeling, orchestration, and engineering practices; partner with enterprise architecture and platform engineering teams on open, interoperable architectures.
Lead the design and implementation of enterprise-scale batch, streaming, and change data capture pipelines using technologies such as Kafka, Spark, Flink, and modern orchestration frameworks.
Enable near-real-time data availability and develop reusable, metadata-driven integration patterns that improve source onboarding and reduce duplication.
Ensure data pipelines meet requirements for throughput, latency, reliability, and recoverability.
Lead engineering delivery of trusted, reusable data products across domains, partnering with domain owners, business teams, and data architects on authoritative sources and integrated data models.
Build standardized, governed data products accessible through APIs, events, and analytical interfaces; enable data connectivity, traceability, and integration across business processes.
Establish practices for data product lifecycle management, versioning, and service-level commitments.
Drive adoption of modern lakehouse architectures, open table formats, and distributed data processing for on-premises 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 performance, scalability, cost, and operational efficiency.
Establish reusable frameworks for ingestion, transformation, data validation, and publishing, and partner with platform teams on consistent data infrastructure engineering practices.
Embed data quality, observability, lineage, and security into engineering workflows; establish automated 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 on classification, access controls, retention, and compliance.
Drive proactive incident prevention, root-cause analysis, and continuous improvement of data reliability.
Enable trusted, contextualized, accessible enterprise data for AI, machine learning, and agentic applications, including semantic layers, knowledge products, retrieval systems, and enterprise AI solutions.
Partner with AI engineering teams to provide reliable data access through governed APIs, structured datasets, and knowledge interfaces; establish secure data exposure patterns for agents that maintain access controls, lineage, and accountability.
Build, lead, and develop a high-performing organization of data engineers, technical leads, and engineering managers.
Establish CI/CD, DataOps, infrastructure as code, automated testing, and production observability; improve 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 and succession plans and foster ownership, accountability, and engineering excellence.