Responsibilities
- Embed with partner engineering and data teams to onboard structured and unstructured data products onto DPaaS from source configuration through production.
- Define data product schemas, ownership, SLAs, quality expectations, and governance attributes.
- Troubleshoot onboarding failures across infrastructure, pipeline, and data layers and validate correctness across raw, staging, and curated layers.
- Run technical onboarding sessions, workshops, office hours, and training to help partner teams self-serve.
- Develop reusable pipeline templates, configuration generators, schema mapping utilities, acquisition connectors, ingestion templates, and transformation scaffolding.
- Contribute to DPaaS platform feature development, framework improvements, testing, and validation of new capabilities.
- Build AI-assisted onboarding workflows using schema inference, automated attribute mapping, data profiling, MCP, AI agents, and Copilot extensions.
- Document onboarding patterns and failure modes, advise partners on data product design, and channel engagement feedback into the platform roadmap.
Requirements
- At least 3 years of data engineering or software engineering experience shipping production-grade solutions.
- Understanding of schema design, data ownership, SLAs, quality frameworks, and data governance for data products.
- Experience with both structured and unstructured data, including orchestration and pipeline tooling.
- Strong Python proficiency; Java or Go knowledge is a plus.
- Familiarity with Azure Data Lake Storage, Azure Blob Storage, Azure Data Factory, Azure-native data services, Snowflake, and cloud-native infrastructure on Azure.
- Experience with container orchestration, declarative infrastructure, application packaging and deployment configuration, and horizontal scaling.
- Some experience working directly with client or partner engineering teams.
- Active use of AI-assisted development tools such as GitHub Copilot, Cursor, Windsurf, or equivalent.
- Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience.
- Preferred qualifications include forward deploy, solutions engineering, or client-embedded engineering experience; financial data platform exposure; data governance, cataloging, or metadata management experience; dbt or data quality framework exposure; unstructured data processing with document parsing, embeddings, vector stores, or blob pipelines; LLM or AI agent framework familiarity; AWS or GCP experience; and CI/CD or DevSecOps experience.
Benefits
- Retirement plan, tuition reimbursement, comprehensive healthcare, support for working parents, and Flexible Time Off.
- Hybrid work model requiring at least 4 days per week in the office and allowing 1 day per week working from home; some groups may require more office time.
Categories
About BlackRock
BlackRock is a global asset manager and technology provider dedicated to helping more and more people experience financial well-being. We help millions of people invest to build savings that serve them throughout their lives. We always start with our clients’ needs and look to offer them more quality choices for how and where to invest their money. Our global investments platform offers our clients access to the world’s markets while making investing easier and more affordable. And with offices in more than 40 countries, our global expertise helps them navigate changing markets to stay ahead of the curve. Follow us for global insights shaping the economy, conversations about financial well-being and more information on culture and careers at BlackRock. https://bit.ly/3n6Fxdy