> Markdown version of [/jobs/ext/4718682-data-engineer](https://www.wearedevelopers.com/jobs/ext/4718682-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Tata Consultancy Services Limited - **Location:** Irving, TX, United States - **Experience:** Expert - **Salary:** $100,000 - $130,000 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Information Engineering, Distributed Computing Environment, Python (Programming Language), Metadata, Metadata Standards, Cloud Services, Standard Sql, Management of Software Versions, Workflow Management Systems, Data Lakes, Information Technology, Lidar, Software Version Control, Data Pipelines, GNSS - **Published:** November 14, 2026 - **Apply:** https://diversityjobs.com/career/18928104/Data-Engineer-Texas-Irving ## About the Role * 5+ years of data engineering experience. * Demonstrated experience processing unstructured and multimodal data, including video, images, LiDAR, GNSS, IMU, and telemetry. * Experience building pipelines that create curated AI training datasets through ingestion, synchronization, transformation, validation, and publishing. * Strong Python and SQL skills with experience in distributed data processing frameworks. * Experience with cloud data platforms, object storage, data lake or lakehouse patterns, workflow orchestration, and version control. * Knowledge of data quality, metadata, lineage, schema management, and reproducible dataset pipelines., Strong ownership, analytical problem solving, communication, and ability to work independently within an agile delivery team, BACHELOR OF COMPUTER SCIENCE ## Description * Design, develop, and maintain scalable pipelines for camera, LiDAR, GNSS, IMU, and telemetry data. * Normalize, synchronize, enrich, and package multimodal data for annotation and AI training. * Implement automated checks for completeness, integrity, calibration, timestamps, formats, and dataset consistency. * Create reusable data models, metadata standards, lineage, and dataset-versioning practices. * Optimize pipeline performance, reliability, observability, and cost. * Troubleshoot data defects and collaborate with AI, annotation, quality, and platform teams. * Document technical designs, operating procedures, and handoffs.