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Tier-1 brand, popular Data Engineer title, mid-level band, and Bangalore metro increase candidate competition.
Technical data engineering skills transfer well across industries, but AFC/financial-crime domain increases role-specific bias.
Mandatory AWS/SQL/Python stack, data modelling skills and AFC/regulatory domain exposure make screening stringent.
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Own technical delivery of data engineering solutions supporting Anti-Financial Crime (AFC) analytics across full data lifecycle: requirements, architecture, pipelines, and deployment.
Design and implement scalable data architectures and ETL/ELT pipelines on AWS, including Postgres, Step Functions, Lambda, Glue, and S3.
Collaborate globally with product owners, compliance teams, and architecture/operations teams, ensuring adherence to enterprise standards and Agile processes.
Proficiency in SQL or Python for data manipulation and analysis.
Hands-on experience with AWS services: Postgres, Step Functions, Lambda, Glue, and S3.
Understanding of data modelling, schema design, and performance tuning.
Work Experience Required: Not explicitly mentioned in the JD.
Mid-level data engineer with a strong foundation in data engineering and analytics, ready for ownership in a global, multi-stakeholder environment.
Experience or familiarity with financial crime domains (ABC, KYC, AML) or financial services, fintech, or regtech sectors is advantageous.
Comfortable working in Agile teams, contributing to technical documentation, code reviews, and sprint planning.