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Tier-1 brand, mid-level generalist data role, metro locations, and broad toolset attract many applicants.
Core data engineering skills transfer widely, but banking governance and domain knowledge raise specialization needs.
Explicit 5+ years, mandatory PySpark/Python/Falcon and banking data governance imply rigorous screening.
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Build, maintain, test, and optimize scalable data architecture and pipelines to support data analysts and scientists.
Collaborate with technology and architecture teams to develop data solutions aligning with the bank's strategic direction and cost management.
Extract, transform, and load data, advocate for product development improvements based on deep knowledge of data structures and metrics.
Minimum 5 years of experience in ETL, data modeling, data warehousing, and integrating data from multiple sources with emphasis on performance, reliability, security, and governance.
Strong expertise in Python, PySpark, and Falcon Framework for scalable data pipeline development and RESTful API creation.
Proficiency in Agile/Scrum methodologies, Git version control, and cloud platforms such as AWS, Azure, or GCP.
Work Experience Required: At least 5 years in relevant data engineering roles.
Experienced in handling large-scale datasets and extracting actionable insights for commercial success.
Familiar with DevOps practices including root cause analysis and proactive issue resolution in data engineering context.
Capable of managing stakeholder communication and collaborating across teams to align data solutions with business and technical strategies.