Forward Deploy Engineer, Aladdin Data, Associate

About this role
About the Role
BlackRock's Enterprise Data Platform (EDP) is the firm's strategic foundation for how data products are built, governed, and consumed at scale, powering investment decisions, risk analytics, and operational workflows across the firm and its global client base.
Data Platform as a Service (DPaaS) is a core capability within EDP, purpose-built to make data product creation fast, reliable, and repeatable. Whether a team is onboarding a new market data feed, publishing a risk dataset, or operationalizing a model output, DPaaS provides the infrastructure, tooling, and guided experience to take a raw data source and turn it into a trusted, production-grade data product. Teams get acquisition, ingestion, transformation, quality validation, and governance without having to build any of it themselves.
Why This Role is Exciting
Most engineers either build platforms or use them. As a Forward Deploy Engineer on the DPaaS team, you do both. You will deploy by embedding directly with teams across the firm, bringing their data products to life and solving real problems that only surface when a platform meets production data. You will build by developing solutions that fill gaps and make it easier for teams to create and publish data products on EDP. You will be at the frontier of how BlackRock thinks about data products, working with real users, influencing what gets built next, and seeing your work in production quickly across a wide range of data domains.
What You Will Do:
Forward Deployment & Data Product Onboarding
Embed directly with partner engineering and data teams to drive end-to-end data product onboarding onto DPaaS, from source configuration through to production
Work hands-on with teams to define data product structure including schema, ownership, SLAs, quality expectations, and governance attributes
Support onboarding of both structured and unstructured data products, adapting approaches to fit the nature of the data
Troubleshoot onboarding failures across infrastructure, pipeline, and data layers in real time
Run technical onboarding sessions and workshops tailored to each team's data product needs
Enable partner teams to self-serve on data product creation over time, reducing dependency on FDE support
Solution Development & Platform Contribution
Develop reusable data product accelerators including pipeline templates, configuration generators, and schema mapping utilities
Build custom acquisition connectors, ingestion templates, and transformation scaffolding for both structured and unstructured data
Contribute to core DPaaS platform engineering efforts including new feature development and framework improvements
Build and maintain data product accelerators and onboarding utilities that become reusable assets across the platform
Client Enablement
Act as a trusted technical advisor on data product design and onboarding best practices for partner engineering and data teams
Run office hours, enablement sessions, and targeted training to help teams build platform confidence independently
Translate partner-specific data requirements into platform-compatible data product configurations
Document onboarding patterns, common failure modes, and solutions into reusable playbooks
Capture and channel structured feedback from onboarding engagements into the DPaaS product and engineering roadmap
AI Assisted Development & Intelligent Data Product Onboarding
Use AI assisted coding tools as a core part of daily workflow, accelerating configuration authoring, pipeline generation, and onboarding automation
Build and contribute to AI assisted onboarding workflows leveraging schema inference, automated attribute mapping, and AI driven data profiling to reduce manual effort
Implement emerging AI tooling including Model Context Protocol (MCP), AI agents, and Copilot extensions to automate repetitive onboarding tasks
Define what AI native data product creation looks like on EDP, contributing patterns that shape the platform roadmap
Feedback Loop & Platform Evolution
Translate real onboarding experiences into structured product feedback that drives platform improvements
Work closely with DPaaS product, engineering, and infrastructure teams to close the loop between partner needs and platform capabilities
Navigate and operate across the full DPaaS technology stack including structured and unstructured data pipelines, Azure Data Lake Storage, Snowflake, Enterprise-grade container orchestration platform supporting declarative infrastructure and horizontal scaling, and Vault
Validate data product correctness and pipeline integrity across raw, staging, and curated data layers
Support testing and validation of new platform capabilities before broader rollout
Required Qualifications:
3+ years of data engineering or software engineering experience with a track record of shipping production-grade solutions
Understanding of data product concepts including schema design, data ownership, SLAs, quality frameworks, and governance
Experience working with both structured and unstructured data
Strong proficiency in Python; working knowledge of Java or Go is a plus
Experience with orchestration and pipeline tooling for structured data (e.g., Directed acyclic graph-based workflow orchestration framework for data and batch processing) and unstructured data processing frameworks
Familiarity with the Azure ecosystem including Azure Data Lake Storage, Azure Blob Storage, Azure Data Factory, and Azure-native data services
Working knowledge of Snowflake including ingestion patterns, database setup, roles, and basic query optimization
Familiarity with Enterprise-grade container orchestration platform supporting declarative infrastructure and horizontal scaling, application packaging and deployment configuration frameworks, and cloud-native infrastructure on Azure
Some experience working directly with client or partner engineering teams in a collaborative or client-facing capacity
Active user of AI assisted development tools (GitHub Copilot, Cursor, Windsurf, or equivalent)
Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience
Preferred Qualifications:
Prior exposure to a forward deploy, solutions engineering, or client-embedded engineering role
Familiarity with financial data platforms or enterprise data ecosystems
Experience with data governance, data cataloging, or metadata management platforms
Exposure to dbt or data quality validation frameworks
Hands-on experience with unstructured data processing including document parsing, embeddings, vector stores, or blob-based data pipelines
Familiarity with LLM based tooling or AI agent frameworks (e.g., LangChain, MCP)
Working knowledge of other cloud platforms (AWS, GCP) and their equivalent data services such as S3, Redshift, BigQuery, and Dataflow
Experience with CI/CD pipelines and DevSecOps practices (Azure DevOps, Declarative GitOps-based continuous delivery system for Kubernetes workloads)
Our benefits
To help you stay energized, engaged and inspired, we offer a wide range of benefits including a strong retirement plan, tuition reimbursement, comprehensive healthcare, support for working parents and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.
Our hybrid work model
BlackRock’s hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person – aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.
Guidance on AI use for candidates
At BlackRock, AI has long been part of how we work – enhancing decision-making, improving operations, and helping us deliver better outcomes for clients. We encourage candidates to use AI thoughtfully to learn, prepare, and work more effectively; but during our interview process, we want to focus on getting to know you through your own experiences, thinking, and judgment. To support you, we’ve provided