Data Engineering Vice President, Data Engineering – BlackRock Data Office

BlackRock•New York, NY

10d•Hybrid

About The Position

The BlackRock Data Office (BDO) advances the firm's enterprise data strategy by building trusted, scalable, and governed data products that power investment, business, operational, and technology outcomes across BlackRock. As a member of the Data Office organization, you will partner closely with product managers, data stewards, platform engineers, software engineers, data scientists, and senior business stakeholders to deliver enterprise data capabilities that enable analytics, reporting, artificial intelligence, machine learning, and digital products. The team is at the forefront of shaping BlackRock's next generation data ecosystem and driving data as a strategic asset across the firm. As a Vice President, Data Engineering, you will provide technical leadership for enterprise-scale data platforms, influence architectural direction, and drive the delivery of high-impact data solutions that support critical business and investment outcomes. You will lead complex engineering initiatives, establish best practices across teams, and serve as a trusted partner to stakeholders across technology and the business. This role offers the opportunity to shape long-term data strategy, mentor engineering talent, and accelerate the firm's adoption of modern data and AI-enabled capabilities.

Requirements

Demonstrated ability to lead complex data-engineering initiatives, influence technical strategy, and collaborate across global, cross-functional teams consisting of engineering, product, governance, and business stakeholders.
Deep expertise in Python, Java, Scala, SQL, Snowflake, cloud-based data platforms, and large-scale distributed data analytics environments, with a proven track record of delivering enterprise-grade solutions.
Strong experience designing and operating high-volume batch and real-time data pipelines, workflow orchestration solutions, data-transformation frameworks, and distributed event streaming architectures.
Expertise in cloud and hybrid technology environments, including AWS, Microsoft Azure, containerized platforms, infrastructure automation, data governance, metadata management, lineage, and enterprise security controls.
Strong understanding of application programming interfaces, data modeling, data-quality frameworks, observability, performance optimization, and modern software-engineering practices supporting scalable platforms.
Experience leading technical teams, mentoring engineers, driving architectural decisions, and influencing organizational standards in a fast-paced, highly collaborative environment.
Exposure to machine learning, artificial intelligence, generative AI, and enterprise data patterns that enable AI-ready products and platforms.

Responsibilities

Lead the architecture, design, and delivery of scalable, resilient, and high-performance data products and pipelines supporting enterprise-wide business, analytics, and investment capabilities.
Drive technical strategy for batch and real-time data ingestion, transformation, and distribution platforms, ensuring alignment with enterprise architecture, governance, security, and data-quality standards.
Establish and advance engineering best practices, reusable frameworks, automation capabilities, CI/CD processes, observability standards, and operational excellence across the data engineering organization.
Partner with senior stakeholders, product leaders, and engineering teams to prioritize strategic initiatives, influence roadmaps, and deliver business outcomes through trusted and accessible data products.
Champion platform scalability, reliability, resiliency, and cost optimization through architectural improvements, performance tuning, and proactive operational management.
Lead complex incident resolution, root-cause analysis, and continuous-improvement efforts while fostering a culture of accountability, innovation, and engineering excellence.
Mentor and develop engineering talent, provide technical leadership across teams, and help define the future direction of BlackRock's enterprise data ecosystem and AI-ready platform capabilities.