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Tier-1 brand, metro Bengaluru location, and popular Data Engineer title increase applicant competition.
Core PySpark and cloud skills are transferable, though banking risk/control experience moderately matters.
Many mandatory technical skills (PySpark, AWS, Databricks, ETL) and AVP expectations raise filtering strictness.
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Build and maintain scalable data pipelines, data warehouses, and data lakes ensuring data accuracy, access, and security.
Design and implement ETL/ELT solutions with PySpark and AWS cloud technologies supporting large-scale enterprise data processing.
Collaborate with data scientists, architects, product owners, and engineering teams to deliver secure, reliable, and performant data products.
Proven experience designing and maintaining scalable data pipelines using PySpark.
Strong hands-on experience with AWS Cloud services such as S3, Glue, EMR, Lambda, EC2, DynamoDB, IAM, CloudWatch, CloudTrail.
Familiarity with modern data platform architectures including Data Lakes and Lakehouses.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in implementing data quality controls, validation frameworks, and reconciliation processes in ETL pipelines.
Skilled in Python programming and familiar with tools like Databricks, Snowflake, Airflow, and Git-based source control.
Capable of collaborating across multiple teams and influencing decision-making for data architecture and operational effectiveness.