Description :

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 efficient 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 : (Mandatory) :

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

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

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