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Tier-1 brand, mid-level generalist role, and likely metro hiring increase competition significantly.
Core data engineering skills transfer across industries, but financial platform familiarity makes fit moderately sensitive.
Explicit 3+ years, mandatory cloud/data stack and client-facing skills increase filter strictness.
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Embed with engineering and data teams to onboard data products onto the Data Platform as a Service (DPaaS), managing schema, SLAs, quality, and governance.
Develop reusable solutions, connectors, templates, and AI-assisted workflows to enable scalable, production-grade data product creation for both structured and unstructured data.
Act as a technical advisor and contributor by running enablement sessions, documenting best practices, capturing feedback, and collaborating across product and engineering teams for continuous platform evolution.
3+ years of data engineering or software engineering experience with production-grade delivery.
Strong proficiency in Python; knowledge of Java or Go is a plus.
Experience with structured and unstructured data, and data product concepts such as schema design, SLAs, data quality, and governance.
Familiarity with Azure data ecosystem (Azure Data Lake Storage, Blob Storage, Data Factory), Snowflake, container orchestration platforms, and use of AI-assisted development tools.
Experienced in both platform development and embedded client-facing deployment or solutions engineering roles, translating complex data requirements into operational data products.
Skilled in working with a broad technology stack including orchestration, unstructured data pipelines, and cloud-native infrastructure on Azure.
Able to operationalize AI tools for automation in data product onboarding, and capable of driving platform improvements through structured feedback and collaboration with cross-functional teams.