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Tier-1 brand, mid-level generalist data role in a metro location increases applicant density.
Core data engineering skills are transferable, but financial platform and onboarding specificity increase domain bias.
Explicit 3+ years, mandatory Azure/Snowflake/Python and client-facing data engineering skills create strict filters.
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Embed with partner engineering and data teams to onboard and deploy production-grade data products on the Data Platform as a Service (DPaaS).
Develop and maintain reusable data product accelerators, custom connectors, and platform features to enhance data product creation and publication.
Translate onboarding experiences into product feedback, run training and enablement sessions, and support AI-assisted automation in data product workflows.
3+ years of experience in data engineering or software engineering with production-grade solutions.
Proficiency in Python; knowledge of Java or Go is a plus.
Experience with structured and unstructured data pipelines, Azure ecosystem (Data Lake Storage, Blob Storage, Data Factory), Snowflake, and container orchestration platforms.
Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience.
Experienced in embedding or deploying data engineering solutions directly with client or partner teams, preferably in a forward deploy or solutions engineering role.
Strong understanding of data product concepts including schema design, data ownership, SLAs, quality, governance, and experience with structured and unstructured data.
Comfortable with AI-assisted development tools and able to integrate emerging AI workflows for automating data product onboarding tasks.