Data Engineer

We are looking for a Data Engineer who can design and build scalable data platforms supporting large-scale enterprise and AI use cases. You'll work across AWS, Python, SQL, and Spark/PySpark, building modern data architectures spanning data lakes, warehouses, and lakehouse platforms. This is a high-ownership role with exposure to architecture, distributed systems, AI/ML teams, and global clients.
Responsibilities:
Design and build scalable data pipelines and ETL/ELT workflows.
Develop high-performance data solutions using Python, SQL, and PySpark/Spark.
Design and implement AWS-based data architectures.
Build and optimize batch and real-time data pipelines.
Work with large-scale data lakes, data warehouses, and lakehouse platforms.
Build reliable data platforms supporting AI/ML and analytics use cases.
Implement data quality, monitoring, and pipeline reliability practices.
Contribute to technical architecture and engineering best practices.
Mentor junior engineers and contribute to technical decision-making.
Collaborate closely with global clients and cross-functional teams.
Requirements:
8-12 years of experience in data engineering/big data engineering.
Strong, hands-on AWS experience is mandatory.
Strong cloud engineering experience.
Strong proficiency in Python and SQL.
Hands-on experience with PySpark / Spark.
Strong ETL/ELT and data pipeline development experience.
Experience with AWS services such as S3 Glue, EMR, Redshift, Athena, Lambda, and 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 experience working with global stakeholders.
Comfortable working the night shift.
Good to have: Palantir Foundry, Kafka, Airflow, Docker/Kubernetes, Terraform, and MLOps/AI data pipelines.

Experience
8-12 yrs