Experience:


- 3+ years of experience working in data engineering or big data environments.

- Experience working with services like EMR, Glue, Redshift within AWS, Snowflake, and Databricks

- Hands -on experience with AWS, Spark, Python, and SQL

- Solid understanding of data modeling and data warehouse design principles

- Experience with distributed data frameworks such as Spark (Core, SQL, Streaming) or Flink

- Hands -on experience with Snowflake or Databricks

- Experience working on setting up data warehouses/ lake or lakehouse platforms end -to -end

- Industry knowledge in Retail, Logistics, FSI, or Manufacturing would be an added advantage

Roles & Responsibilities:

- Develop, maintain,



and optimize ETL pipelines for batch and streaming data

- Work with data architects to implement scalable data models and warehouse structures

- Write effective SQL and PySpark code for data transformation and analytics

- Support data quality, governance, and monitoring processes

- Collaborate with business and analytics teams to understand data requirements and ensure timely data delivery

- Participate in performance tuning and troubleshooting of data workflows

- Contribute to documentation, standards, and automation across data engineering projects

- Collaborate with cross -functional teams to ensure seamless data delivery and alignment with business goals

Key Skills:

- Technologies: AWS, Spark, Python, SQL, Airflow, Kafka, Snowflake, Databricks

- Frameworks: Spark Core, Spark SQL, Spark Streaming/Flink

- Soft Skills: Customer communication, Team coordination, problem -solving, Ownership