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Tier-1 brand, mid-level generalist data role, metro hiring, and broad multi-skill requirements increase competition.
Data-platform and cloud skills are transferable, though finance-specific experience increases preference.
Explicit 3+ years plus mandatory Azure, Snowflake, Python, and client-facing skills, making filters strict.
Embed with partner teams to onboard and operationalize data products on BlackRock's Data Platform as a Service (DPaaS), handling schema, SLAs, quality, and governance.
Develop reusable data product accelerators, connectors, pipelines, and contribute to core platform engineering for structured and unstructured data.
Serve as technical advisor and run enablement sessions to empower partner teams toward self-service data product creation, incorporating AI-assisted workflows to optimize onboarding processes.
3+ years of data engineering or software engineering experience with production-grade solutions.
Proficiency in Python; working knowledge of Java or Go is a plus.
Experience with structured and unstructured data processing including orchestration and pipeline tooling (e.g., DAG workflow orchestration).
Familiarity with Azure data services (Data Lake Storage, Blob Storage, Data Factory), Snowflake, container orchestration platforms, and active use of AI-assisted development tools.
Bachelor's or Master's degree in Computer Science, Engineering or equivalent practical experience.
Experienced in data product lifecycle with strong skills in schema design, data governance, SLAs, and quality validation across varied data domains.
Comfortable working directly with engineering and data teams in client-facing or embedded roles, influencing platform evolution based on real-world onboarding data.
Skilled in AI-assisted development workflows, able to integrate emerging AI tools like Model Context Protocol and automated schema inference into data product creation.