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Tier-1 brand, remote option, mid-level experience requirement, and Mumbai metro raise applicant competition.
Data engineering skills broadly transferable, but financial data governance and embedded platform experience increase sensitivity.
Explicit 3+ years plus Azure, Snowflake, Python, and platform onboarding skills increase screening strictness.
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Embed with internal partner teams to onboard and deploy data products onto the Data Platform as a Service (DPaaS), handling end-to-end configuration, troubleshooting, and production readiness.
Develop reusable tooling and accelerators for data product creation including pipeline templates, connectors, and schema utilities supporting both structured and unstructured data.
Serve as a technical advisor and feedback channel, running enablement sessions and contributing product insights to evolve the DPaaS platform and integrate AI-assisted onboarding capabilities.
3+ years of data engineering or software engineering experience with production-grade solution delivery.
Proficiency in Python; working knowledge of Java or Go is a plus.
Experience with orchestration/pipeline tools, Azure data services (Data Lake, Blob Storage, Data Factory), Snowflake, and container orchestration platforms.
Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience; Location requirement: Mumbai, India.
Experienced in both development and client-facing data engineering roles involving structured and unstructured data onboarding at scale.
Skilled in translating partner-specific data needs into platform-compatible configurations and thriving in embedded, cross-team collaboration environments.
Comfortable leveraging AI-assisted development tools and contributing to AI-native data product workflows to reduce manual effort and accelerate onboarding.