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Tier-1 brand, common data engineer title, metro location, and broad skillset make hiring highly competitive.
Core data engineering skills transfer across industries, but bank risk, controls and governance increase domain specificity.
Senior title plus specific Databricks and AWS requirements create moderate shortlisting strictness.
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Build and maintain data architectures and pipelines ensuring data is durable, complete, consistent, and secure.
Design and implement data warehouses and lakes handling required data volumes, velocity, and security measures.
Collaborate with data scientists to build and deploy machine learning models while advising on operational effectiveness and risk management.
Experience required with Databricks in data engineering, analytics, and platform implementation.
Strong programming skills in Python for developing, testing, and maintaining scalable data solutions.
Experience with AWS development services such as Lambda, Glue, Step Functions, IAM roles, Lake Formation, Event Bridge, SNS, SQS, EC2, Security Groups, CloudFormation, RDS, DynamoDB, and Redshift.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in leading complex data engineering projects with cross-functional collaboration and influencing stakeholders.
Comfortable working in regulated financial environment with risk mitigation and control ownership.
Strong expertise in enterprise software design and multiple data formats, and familiar with streaming services like Kafka or Kinesis.