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Tier-1 brand, mid-senior level, metro location and common Data Engineer skills increase competition.
Core data engineering skills are transferable, though financial services experience is preferred.
Explicit 8–11 years plus required Databricks, AWS, Spark, and data architecture mandates strict shortlisting.
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Lead design and governance of scalable cloud data architecture and platforms supporting analytics and investment management.
Develop and deliver ETL/ELT pipelines using Python, Spark, and Databricks on AWS with focus on data quality, security, and performance.
Provide technical leadership and mentorship to engineering teams, driving cloud modernization and CI/CD best practices.
8–11 years of experience in Data Engineering, Data Architecture, or related roles.
Strong expertise and hands-on experience with AWS, Databricks, Python, Spark, SQL, and data modeling.
Experience in data warehousing and Lakehouse architecture, cloud data engineering, and solution design.
Work Experience Required: 8–11 years; Financial Services or Asset Management experience preferred but not mandatory; Notice period: Not explicitly mentioned in the JD.
Experienced in leading cloud data platform architecture and engineering teams with both hands-on and leadership skills.
Demonstrated ability to manage enterprise-level data solutions with focus on scalability, security, and compliance, preferably in Financial Services.
Familiar with DevOps practices and automation in cloud environments, able to influence stakeholders and drive best practices adoption.