Design, develop, and maintain ETL processes using SQL Server Integration Services (SSIS). Create and optimize complex SQL queries, stored procedures, and data transformation logic on Oracle and SQL Server databases. Build scalable and reliable data pipelines using AWS services (e.g., S3, Glue, Lambda, RDS, Redshift). Develop and maintain Linux shell scripts to automate data workflows and perform system-level tasks. Schedule, monitor, and troubleshoot batch jobs using tools like Control-M, AutoSys, or cron. Collaborate with stakeholders to understand data requirements and deliver high-quality integration solutions. Ensure data quality, consistency, and security across systems. Maintain detailed documentation of ETL processes, job flows, and technical specifications. Experience with job scheduling tools such as Control-M and/or AutoSys. Exposure to version control tools (e.g., Git) and CI/CD pipelines. Show more Show less

Job Summary: We are looking for a Data Engineer with solid hands-on experience in Azure-based data pipelines and Snowflake to help build and scale data ingestion, transformation, and integration processes in a cloud-native environment. Key Responsibilities: Develop and maintain data pipelines using ADF, Snowflake, and Azure Storage Perform data integration from various sources including APIs, flat files, and databases Write clean, optimized SQL and support data modeling efforts in Snowflake Monitor and troubleshoot pipeline issues and data quality concerns Contribute to documentation and promote best practices across the team Qualifications: 3–5 years of experience in data engineering or related role Strong hands-on knowledge of Snowflake, Azure Data Factory, SQL, and Azure Data Lake Proficient in scripting (Python preferred) for data manipulation and automation Understanding of data warehousing concepts and ETL/ELT patterns Experience with Git, JIRA, and agile delivery environments is a plus Strong attention to detail and eagerness to learn in a collaborative team setting Show more Show less

Key Responsibilities: Build and enhance scalable data pipelines using Azure Data Factory, Snowflake, and Azure Data Lake Develop and maintain ELT processes to ingest and transform data from various structured and semi-structured sources Write optimized and reusable SQL for complex data transformations in Snowflake Collaborate closely with analytics teams to ensure clean, reliable data delivery Monitor and troubleshoot pipeline performance, data quality, and reliability Participate in code reviews and contribute to best practices around data engineering standards and governance Qualifications: 5+ years of data engineering experience in enterprise environments Deep hands-on experience with Snowflake, Azure Data Factory, Azure Blob/Data Lake, and SQL Proficient in scripting for data workflows (Python or similar) Strong grasp of data warehousing concepts and ELT development best practices Experience with version control tools (e.g., Git) and CI/CD processes for data pipelines Detail-oriented with strong problem-solving skills and the ability to work independently Show more Show less

We need a ETL engineer for MS SQL Server Integration Service working in 4:30am to 10:30am IST shift timings. Design, develop, and maintain ETL processes using SQL Server Integration Services (SSIS). Create and optimize complex SQL queries, stored procedures, and data transformation logic on Oracle and SQL Server databases. Build scalable and reliable data pipelines using AWS services (e.g., S3, Glue, Lambda, RDS, Redshift). Develop and maintain Linux shell scripts to automate data workflows and perform system-level tasks. Schedule, monitor, and troubleshoot batch jobs using tools like Control-M, AutoSys, or cron. Collaborate with stakeholders to understand data requirements and deliver high-quality integration solutions. Ensure data quality, consistency, and security across systems. Maintain detailed documentation of ETL processes, job flows, and technical specifications. Experience with job scheduling tools such as Control-M and/or AutoSys. Exposure to version control tools (e.g., Git) and CI/CD pipelines. Show more Show less