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Tier-1 brand and metro location with a common data engineering title suggest elevated competition.
Data engineering skills are highly transferable across industries, though enterprise payments context creates moderate domain specificity.
No explicit years but mandatory Databricks, SQL/Python, and managerial requirements imply medium strictness.
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Lead design and delivery of scalable data pipelines and platforms supporting analytics, reporting, and AI use cases.
Manage and mentor a team of data engineers while overseeing end-to-end delivery of data engineering projects.
Collaborate with cross-functional teams to ensure data quality, governance, security, and operational stability of workflows.
Experience in data engineering with ETL/ELT pipeline development.
Strong proficiency in SQL and Python.
Familiarity with data platforms such as Databricks and knowledge of data architecture concepts like Lakehouse and Warehousing.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in managing multiple analytics projects and leading data engineering teams within large enterprises.
Skilled at stakeholder management and translating business requirements into technical solutions.
Comfortable working with large datasets and collaborating across functions including governance and AI/ML teams.