Google data engineer

Job description

Dear Candidates, Greetings from TCS!!!! TCS is looking for Google Data Engineer Experience: 7-10 years Location: Bangalore Date of interview: 02/07/2026 Mode of interview: Virtual JOB DESCRIPTION: - Experience level of 7 to 10 years in data engineering, data warehousing, or a related field. - Experience with dashboarding tools like plx dashboard and looker studio - Experience with building data pipelines, reports, best practices and frameworks. - Experience with design and development of scalable and actionable solutions (dashboards, automated collateral, web applications). - Experience with code refactoring for optimal performance. - Experience writing and maintaining ETLs which operate on a variety of structured and unstructured sources. - Familiarity with non-relational data storage systems (NoSQL and distributed database management systems). Required Skills: - Strong proficiency in SQL, NoSQL, ETL tools, BigQuery and at least one programming language (e.g., Python, Java). - Big Query,Data Flow,Data Proc,Cloud Sql,Teraform etc - Strong understanding of data structures, algorithms, and software design principles. - Experience with data modeling techniques and methodologies. - Proficiency in troubleshooting and debugging complex data-related issues. - Ability to work independently and as part of a team. Responsibilities: - **Data Pipeline Development:** Design, implement, and maintain robust and scalable data pipelines to extract, transform, and load data from various sources into our data warehouse or data lake. - **Data Modeling and Warehousing:** Collaborate with data scientists and analysts to design and implement data models that optimize query performance and support complex analytical workloads. - **Cloud Infrastructure:** Leverage Google Cloud and other internal storage platforms to build and manage scalable and cost-effective data storage and processing solutions. - **Data Quality Assurance:** Implement data quality checks and monitoring processes to ensure the accuracy, completeness, and consistency of data. - Build large scale data and analytics solutions on GCP, Efficiently use the GCP platform to integrate large datasets from multiple data sources, analyse data, data modelling, data exploitation/visualization, DevOps, CI/CD implementation Build automated data pipelines and work in Data engineering solution on GCP using Cloud BigQuery, Cloud DataProc, - **Performance Optimization:** Continuously monitor and optimize data pipelines and queries for performance and efficiency. - **Collaboration:** Work closely with data scientists, analysts, and other stakeholders to understand their data needs and deliver solutions that meet their requirements.