Data Engineer II Job in For a Client of TeamLease Digital at Bangalore

We are looking for an experienced Data Engineer with strong expertise in Snowflake, AWS, SQL, and data engineering to design, develop, and maintain scalable data pipelines and cloud-based data solutions. The candidate will work with cross-functional teams to build reliable and high-performance data platforms supporting business and analytics requirements.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL/ELT workflows.

• Develop and optimize data solutions using Snowflake and AWS cloud services.

• Build data ingestion and transformation pipelines from multiple structured and unstructured sources.

• Develop complex SQL queries, stored procedures, views, and data transformations.

• Implement data loading, cleansing, validation, and reconciliation processes in Snowflake.

• Work with AWS services such as S3, Lambda, Glue, and CloudWatch.

• Develop and maintain automated data pipelines using Python and/or relevant ETL tools.

• Optimize Snowflake queries, warehouse usage, and data models for performance and cost efficiency.

• Implement data quality, monitoring, error handling, and troubleshooting mechanisms.

• Collaborate with Data Architects, BI Developers, Analysts, and application teams.

• Participate in code reviews, testing, deployment, and production support.

• Follow data security, governance, and compliance standards.

Mandatory Skills

• 3–6 years of experience in Data Engineering.

• Strong hands-on experience with Snowflake.

• Strong proficiency in SQL and data transformation.

• Good hands-on experience with AWS cloud services.

• Experience with AWS S3, AWS Glue, Lambda, and/or related AWS data services.

• Strong programming/scripting experience in Python.

• Experience developing ETL/ELT data pipelines.

• Good understanding of data warehousing concepts, dimensional modeling, and data integration.

• Experience with performance tuning and optimization in Snowflake.

• Good understanding of data quality, validation, and reconciliation processes.