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Description supplied by the original job listing.
Responsibilities:
Design and build scalable data pipelines and ETL/ELT workflows.
Develop data solutions using Python, SQL and PySpark/Spark.
Design and implement AWS-based data architectures.
Work with large-scale data across Data Lakes, Data Warehouses and Lakehouse platforms.
Build and optimise batch and real-time data pipelines.
Implement data quality, monitoring and pipeline reliability practices.
Work with AI/ML teams to build data platforms supporting AI and analytics use cases.
Contribute to technical architecture and mentor junior engineers.
Collaborate with global clients and cross-functional teams.
Requirements:
8-12 years of experience in Data Engineering / Big Data Engineering.
Strong AWS experience - Mandatory.
Strong Cloud experience - Mandatory.
Strong hands-on Python and SQL.
Experience with PySpark / Spark.
Strong ETL/ELT and data pipeline development experience.
Experience with AWS services such as S3 Glue, EMR, Redshift, Athena, Lambda, Kinesis.
Experience with Snowflake / Databricks / Redshift or similar data platforms.
Strong understanding of data modelling and distributed systems.
Experience with CI/CD and DevOps practices.
Strong communication skills and ability to work with global stakeholders.
Comfortable working in the 4:00 PM - 1:00 AM IST shift.
Good to have: Palantir Foundry, Kafka, Airflow, Docker/Kubernetes, Terraform, MLOps/AI data pipelines.
Experience
8-12 yrs
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