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Tier-1 brand, generalist Data Engineer title, metro location, and broad AWS/PySpark skillset.
Low — core PySpark, AWS, and ETL skills are highly transferable across industries.
High due to mandatory PySpark/AWS/Databricks skills plus governance and risk-control expectations.
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Design, develop, and maintain scalable ETL data pipelines and data architectures using PySpark and AWS Cloud technologies.
Build and optimize data solutions including data warehouses and data lakes ensuring security, data quality, and large-scale data processing capabilities.
Collaborate with stakeholders and data scientists to deliver secure, performant data products and support machine learning model deployment.
Strong expertise in PySpark and hands-on experience with AWS Cloud services (e.g., S3, Glue, EMR, Lambda).
Experience with ETL/ELT development, data architecture design, and large-scale distributed data processing.
Mandatory location: Bengaluru, India.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in developing reusable frameworks and implementing data quality controls within modern data platform architectures (Data Lake, Lakehouse).
Capable of leading complex projects or teams, influencing policy, and managing risks related to data engineering work.
Familiar with cloud-native tools such as Databricks, Snowflake, Airflow, and Git-based source control, with a track record of operational effectiveness.