Data Engineer, Vice President
BlackRock is hiring a Data Engineer, Vice President in London, United Kingdom. Level rates it ; you can apply on Level.
AI in this role
About this role
Company Intelligence is Preqin's private company data platform, providing structured, auditable company-level data and analytics across the private markets investment lifecycle. The platform integrates company, deal, and ownership data with fund, manager, and performance information to deliver a connected view of private capital activity. Built on granular data and rigorous methodologies, Company Intelligence enables scalable analysis across deal sourcing, due diligence, valuation benchmarking, and comparative research.
You will join BlackRock's Private Markets Data Engineering organization, working on a strategically important platform at the center of Preqin's private markets data strategy. The team is responsible for expanding and enhancing Preqin's global private company coverage while building scalable data products and analytics capabilities that increase transparency across private markets.
Your role and impact
As a Vice President within the Company Intelligence Data Engineering team, you will design, build, and operate scalable, production-grade data platforms and pipelines that underpin the growth of Preqin's private company universe. You will serve as a player-coach, combining deep technical expertise with people leadership to deliver high-impact data solutions that support investment research, analytics, and decision-making across the private markets ecosystem.
You will provide technical leadership across complex engineering initiatives, partner closely with product, analytics, and business stakeholders, and help establish engineering standards that promote reliability, scalability, governance, and operational excellence. Your work will directly contribute to expanding data coverage, improving data quality, and enabling advanced analytics across the Preqin platform.
Your responsibilities
In every role at BlackRock, you'll be expected to apply sound judgement and critical thinking to solve complex problems, adapt as the business evolves, and combine the curiosity to explore new approaches and technologies with the rigor to challenge the results. The scope of this role also includes the following responsibilities:
- Provide senior technical leadership and architectural direction for data platforms and pipelines that support the scalable expansion of Preqin's private company universe while ensuring performance, cost efficiency, and maintainability.
- Own the end-to-end delivery of complex, business-critical data engineering initiatives, from solution design through production operation, with accountability for reliability, quality, and business outcomes.
- Drive a platform-centric engineering strategy by developing reusable frameworks, shared datasets, and extensible data products that support multiple Company Intelligence use cases.
- Establish and maintain high standards for data quality, governance, lineage, observability, and operational risk management across enterprise-grade data assets.
- Partner with product, analytics, and business stakeholders to translate strategic objectives into scalable data solutions that deliver measurable business value.
- Lead, coach, and develop a team of data engineers while maintaining hands-on technical contribution and fostering a collaborative, high-performing engineering culture.
Your experience
- Demonstrated experience designing, building, and operating enterprise-scale data platforms, applying sound architectural judgement across scalability, governance, cost efficiency, reliability, and long-term sustainability.
- Advanced expertise in SQL, analytical data modelling, and modern analytics engineering practices, including transformation frameworks, testing, documentation, and embedded data quality controls.
- Strong proficiency in Python and data pipeline engineering, including orchestration, automation, dependency management, observability, and operational resilience in production environments.
- Experience designing and operating cloud-native data platforms and enterprise data warehouses, supported by modern platform engineering practices such as infrastructure as code and container orchestration.
- Experience leading, mentoring, and developing data engineers while maintaining strong hands-on technical credibility.
- Experience working with private markets or alternative investment data, supporting investment, research, or portfolio analytics at scale.
- Exposure to advanced analytics and artificial intelligence-enabled capabilities, including automation, model-driven data products, and intelligent workflow solutions.
Our benefits
To help you stay energized, engaged and inspired, we offer a wide range of employee benefits including: retirement investment and tools designed to help you in building a sound financial future; access to education reimbursement; comprehensive resources to support your physical health and emotional well-being; family support programs; 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 guidance on when and how to use AI during our hiring process so you can approach each step with confidence and showcase your best self.
About BlackRock
At BlackRock, we are all connected by one mission: to help more and more people experience financial well-being. Our clients, and the people they serve, are saving for retirement, paying for their children’s educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress.
This mission would not be possible without our smartest investment – the one we make in our employees. It’s why we’re dedicated to creating an environment where our colleagues feel welcomed, valued and supported with networks, benefits and development opportunities to help them thrive.
To learn more about BlackRock, please visit Careers.BlackRock.com. We also encourage you to get to know us on LinkedIn, Instagram, YouTube, X, and TikTok.
BlackRock is proud to be an Equal Opportunity Employer. We evaluate qualified applicants without regard to age, disability, race, religion, sex, sexual orientation and other protected characteristics at law.
How we rate this
Data Engineer, Vice President at BlackRock rates 31 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
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Pay
- Typical data engineer job
- $149k
This posting doesn't list pay. Use the typical data engineer figure when pay comes up. See data engineer jobs
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