[Skip to main content](#main-content) [OpenNash Jobs](https://jobs.opennash.com/) [Hiring report](/reports/hiring/) · [All Tata Consultancy Services jobs](https://jobs.opennash.com/companies/tata-consultancy-services/) · [Search jobs](/search/) Tata Consultancy Services · Chennai # GCP Data Engineer Chennai Data & Analytics Last seen on Tata Consultancy Services’s official careers site on August 1, 2026; it may have closed since. [Apply on Tata Consultancy Services’s careers site →](https://ibegin.tcsapps.com/candidate/next/en-IN/jobs/380714J) [OpenNash Jobs](https://jobs.opennash.com/) / [Tata Consultancy Services](https://jobs.opennash.com/companies/tata-consultancy-services/) / GCP Data Engineer ## Job description ### Required Skills Expertise in Google Data Engineering services – Big Query, Dataproc etc Strong experience in programming languages such as Python, PySpark data manipulation and engineering tasks. Expertise in SQL and NoSQL databases In-depth knowledge of data warehousing solutions Deep understanding on data warehousing concepts Proficiency in designing, developing, and maintaining ETL (Extract, Transform, Load) processes using tools like Apache Airflow, Talend, or Informatica. ### Good to have skills Understanding of DevOps Concepts in GCP. Strong understanding of data modeling concepts and techniques to create efficient and scalable data models. Experience with version control systems such as Git for code management and collaboration. Knowledge of data governance, data quality standards, and data security practices to ensure compliance and protection of sensitive information. SN Responsibility of / Expectations from the Role 1 - Design, develop, and maintain scalable data pipelines for extracting, transforming, and loading data from various sources to ensure seamless data flow and accessibility. 2 - Collaborate with cross-functional teams to integrate data from multiple disparate sources, ensuring consistency, accuracy, and reliability of data. 3 - Optimize data processing workflows and storage solutions for performance, scalability, and cost-efficiency 4 - Implement data quality checks and validation processes to ensure the accuracy, completeness, and consistency of data throughout the data lifecycle. 5 - Monitor the performance of data pipelines and infrastructure, identifying and resolving issues to maintain system stability and reliability.