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Mid-level metro Data Engineer with common PySpark/cloud skills and known bank brand increases competition.
Core data engineering skills are transferable, though banking governance/security knowledge moderately increases domain specificity.
Explicit 5+ years requirement plus mandatory PySpark/Python/Falcon and cloud skills enforce strict shortlisting.
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Build, maintain, test, and optimize scalable data architecture and pipelines using Python, PySpark, and Falcon Framework.
Extract, transform, and load data into data platforms to support analysts and scientists, ensuring performance, reliability, and data quality.
Collaborate with technology and architecture teams and manage data platform cost levers to develop cost-effective solutions in line with the bank's strategic direction.
At least 5 years of experience in ETL, data modeling, data warehousing, and integration from multiple sources focusing on performance, security, and governance.
Strong expertise in Python, PySpark, Falcon Framework, Agile/Scrum methodologies, Git, and cloud platforms such as AWS, Azure, or GCP.
Experience with building scalable data pipelines and developing RESTful APIs.
Work Experience Required: Minimum 5 years explicit in JD.
Deep understanding of large-scale data extraction and usage to drive customer value and product development.
Experience in adopting DevOps practices within data engineering and proactive problem resolution.
Ability to engage and collaborate effectively with diverse stakeholders across technology, architecture, and business teams.