Description

Design and build modern data architectures and automated ETL/ELT workflows to support analytical, operational, and AI/ML workloads.

This role is on-site.

Responsibilities

• Design, build, and maintain end-to-end ETL/ELT pipelines to ingest data into Snowflake and AWS data lakes.
• Develop modular ETL components handling large volumes, implementing incremental loads and CDC.
• Design and implement data lakehouse and warehouse architectures using AWS S3, Glue, and Snowflake.
• Optimize Snowflake performance through partitioning, clustering, and query tuning.
• Automate data workflows using orchestration tools and implement CI/CD pipelines for ETL code deployment.

Required Skills

• 8–10 years of hands-on experience in data engineering and ETL/ELT development within cloud environments.
• Strong proficiency in Snowflake, covering schema design, query optimization, and performance tuning.
• Deep expertise with ETL/ELT tools such as AWS Glue, dbt, Informatica, Talend, or Apache NiFi.
• Experience with the AWS data stack, including S3, Glue, Step Functions, and IAM.
• Advanced SQL skills and demonstrable experience in Python, Java, or Scala for data transformation.
• Solid understanding of data modeling techniques: 3NF, Star Schema, Snowflake Schema, or Data Vault 2.0.
• Experience managing data lineage, metadata, and schema evolution.
• Proficiency in Shell Scripting.

Preferred Skills

• Knowledge of Data Lake concepts and working with diverse data sources.

Key Skills

Education

Any Graduate