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Tier-1 brand, popular data engineer title, metro locations, and broad required skills increase candidate competition.
Medium — strong transferable data engineering skills but banking governance and security increase domain specificity.
High due to explicit eight-year requirement and mandatory PySpark, Kafka, AWS, and data governance skills.
Develop and maintain scalable data pipelines and data products using PySpark, SQL, MongoDB, and Kafka on AWS cloud platforms.
Lead design and planning of complex data engineering products with focus on cost-effective, secure, and operationally excellent solutions applying lakehouse architecture and data governance.
Drive automation in data engineering pipelines, removing manual stages, and collaborate across teams to support strategic data initiatives and product development.
At least 8 years of experience in ETL design, data quality testing, cleansing, sourcing, data warehousing, and data modelling.
Proficient in building scalable data pipelines using PySpark, SQL, MongoDB, Kafka, and experience with AWS cloud and lakehouse architectures.
Experience with programming languages and strong knowledge of modern code development practices, automation, CI/CD.
Work Experience Required: Minimum 8 years; Notice period: Not explicitly mentioned in the JD.
Experienced data engineer with a strong background in end-to-end data product delivery on AWS using lakehouse architecture and data governance frameworks.
Able to lead complex product design and guide colleagues, applying a configuration-first engineering approach with low-code, reusable components, and automation.
Capable of engaging a wide range of stakeholders proactively and aligning technical delivery with business needs to drive customer value.