Google Data Engineer

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 Experience:

• 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. Roles & 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.