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Tier-1 brand, metro location, common Data Engineer title, and broad AWS/PySpark/Kafka requirements increase competition.
Core data engineering skills are transferable, but banking data governance and lakehouse experience increases domain specificity.
Mandatory 8+ years plus required PySpark, Kafka, AWS, ETL, governance and lakehouse expertise enforces high shortlisting strictness.
Develop and maintain scalable, cost-effective data pipelines and data products using technologies like PySpark, SQL, MongoDB, Kafka, and AWS.
Lead complex data engineering tasks including data extraction, transformation, and architecture design to support analysts and data scientists.
Drive delivery of data solutions by applying lakehouse architecture, data governance, security controls, and fostering automation with CI/CD and low-code frameworks.
At least 8 years of experience in ETL design, data quality testing, cleansing, monitoring, sourcing, analysis, data warehousing, and data modelling.
Strong technical skills in PySpark, SQL, MongoDB, Kafka, AWS, and experience delivering CDC solutions on cloud platforms.
Proven ability to own end-to-end data product delivery and implement scalable, secure, and governed data architectures.
Location requirement: Must be based in India for all normal working days.
Experienced senior-level data engineer capable of leading complex product design and providing technical guidance to teams.
Proficient in building high-quality, reusable data solutions leveraging automation, low-code frameworks, and modern code development practices.
Able to engage proactively with diverse stakeholders to understand complex business problems and deliver customer-focused data insights.