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Tier-1 brand, popular data-engineer role, metro locations, and broad skillset requirements increase competition.
Core data engineering skills are transferable, but banking governance and product context require domain experience.
Explicit 8+ years requirement plus many mandatory technologies and governance expectations increases screening rigor.
Develop and manage scalable data engineering pipelines and models, focusing on ETL design and data transformation to support analytics and product development needs.
Lead the design and delivery of complex data products on AWS, ensuring cost-effective, secure, and maintainable data architectures with automation and CI/CD practices.
Drive cross-team collaboration by advocating for product improvements and contributing to strategic data engineering initiatives within the organization.
Minimum 8 years of experience in ETL technical design, data quality assurance, data sourcing, and data warehousing/modeling.
Proficient in building data pipelines using PySpark, SQL, MongoDB, and Kafka with experience on cloud platforms, specifically AWS.
Experience in applying lakehouse architecture, data governance, security controls, and operational excellence in data product delivery.
Work Experience Required: At least 8 years; Notice Period: Not explicitly mentioned in the JD
Experienced in end-to-end data product ownership on cloud platforms with a focus on automation, reusable components, and CI/CD workflows.
Demonstrates strategic understanding of data platform cost optimization combined with advanced technical skills in data engineering and architecture.
Capable of engaging with diverse stakeholders including analysts, data scientists, and product teams to translate complex business requirements into effective data solutions.