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Tier-1 brand, metro location, and mid-level generalist data role attract high applicant density.
Data engineering skills are transferable, though financial data platform experience increases domain specificity.
Mandatory 3+ years plus Azure, Snowflake, and data product experience tightens filters.
Embed with partner teams to drive end-to-end data product onboarding onto DPaaS, managing schema, SLAs, quality, and governance for both structured and unstructured data.
Develop and maintain reusable data product accelerators, connectors, and onboarding utilities to improve platform scalability and ease of use.
Act as a technical advisor and run enablement sessions, while translating onboarding experience into product feedback to evolve the DPaaS platform, leveraging AI assisted development tools.
3+ years of data engineering or software engineering experience with production-grade solutions.
Strong proficiency in Python; working knowledge of Java or Go is a plus.
Experience with data orchestration and pipeline tooling for structured and unstructured data, plus familiarity with Azure Data Lake, Snowflake, and container orchestration platforms on Azure.
Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience.
Experienced in embedding with and enabling partner engineering or data teams through hands-on support and training.
Skilled at balancing solution development with client-facing roles to influence platform roadmap and improve user experience.
Comfortable leveraging AI tools for automation and productivity, and knowledgeable about enterprise-scale data ecosystems including governance and unstructured data processing.