Vice President, Forward Deploy Sr. Data Engineer

About this roleAbout the RoleBlackRock'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 ExcitingMost 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 OnboardingEmbed directly with partner engineering and data teams to drive end-to-end data product onboarding onto DPaaS, from source configuration through to productionWork hands-on with teams to define data product structure including schema, ownership, SLAs, quality expectations, and governance attributesSupport onboarding of both structured and unstructured data products, adapting approaches to fit the nature of the dataTroubleshoot onboarding failures across infrastructure, pipeline, and data layers in real timeRun technical onboarding sessions and workshops tailored to each team's data product needsEnable partner teams to self-serve on data product creation over time, reducing dependency on FDE supportSolution Development & Platform ContributionDevelop reusable data product accelerators including pipeline templates, configuration generators, and schema mapping utilitiesBuild custom acquisition connectors, ingestion templates, and transformation scaffolding for both structured and unstructured dataContribute to core DPaaS platform engineering efforts including new feature development and framework improvementsBuild and maintain data product accelerators and onboarding utilities that become reusable assets across the platformClient EnablementAct as a trusted technical advisor on data product design and onboarding best practices for partner engineering and data teamsRun office hours, enablement sessions, and targeted training to help teams build platform confidence independentlyTranslate partner-specific data requirements into platform-compatible data product configurationsDocument onboarding patterns, common failure modes, and solutions into reusable playbooksCapture and channel structured feedback from onboarding engagements into the DPaaS product and engineering roadmapAI Assisted Development & Intelligent Data Product OnboardingUse AI assisted coding tools as a core part of daily workflow, accelerating configuration authoring, pipeline generation, and onboarding automationBuild and contribute to AI assisted onboarding workflows leveraging schema inference, automated attribute mapping, and AI driven data profiling to reduce manual effortImplement emerging AI tooling including Model Context Protocol (MCP), AI agents, and Copilot extensions to automate repetitive onboarding tasksDefine what AI native data product creation looks like on EDP, contributing patterns that shape the platform roadmapFeedback Loop & Platform EvolutionTranslate real onboarding experiences into structured product feedback that drives platform improvementsWork closely with DPaaS product, engineering, and infrastructure teams to close the loop between partner needs and platform capabilitiesNavigate and operate across the full DPaaS technology stack including structured and unstructured data pipelines, Azure Data Lake Storage, Snowflake, Kubernetes, and VaultValidate data product correctness and pipeline integrity across raw, staging, and curated data layersSupport testing and validation of new platform capabilities before broader rolloutRequired Qualifications: 7+ years of data engineering or software engineering experience with a track record of shipping production-grade solutionsBachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experienceStrong understanding of data product concepts including schema design, data ownership, SLAs, quality frameworks, and governanceExperience working with both structured and unstructured dataStrong proficiency in Python; working knowledge of Java or Go is a plusExperience with orchestration and pipeline tooling for structured data (e.g., Apache Airflow) and unstructured data processing frameworksDeep familiarity with the Azure ecosystem including Azure Data Lake Storage, Azure Blob Storage, Azure Data Factory, and Azure-native data servicesExperience with Snowflake across the full data lifecycle including ingestion, transformation, data sharing, dynamic tables, streams, tasks, and SnowparkFamiliarity with Kubernetes, Helm, and cloud-native infrastructure on AzureDemonstrated experience working directly with client or partner engineering teams in a client-facing or embedded capacityActive user of AI assisted development tools (GitHub Copilot, Cursor, Windsurf, or equivalent)Preferred Qualifications: Prior experience in a forward deploy, solutions engineering, or client-embedded engineering roleFamiliarity with financial data platfor