Data Engineer, Vice President

Preqin is a leading provider of private markets data and analytics and part of BlackRock. It delivers trusted data, benchmarks, and insights that support decision‑making across the full private markets investment lifecycle.

With comprehensive global coverage across private equity, private credit, real estate, infrastructure, venture capital, and hedge funds, Preqin brings greater transparency to private markets through rigorous data collection, deep market expertise, and continuously evolving methodologies. Its data and technology underpin how investors raise capital, evaluate opportunities, monitor portfolios, and assess performance at scale.

Company Intelligence is Preqin’s private company data platform, providing structured, auditable company‑level data and analytics across the private markets investment lifecycle. It 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 robust methodologies, Company Intelligence supports scalable analysis across deal sourcing, due diligence, valuation benchmarking, and comparative research. By increasing transparency across the private company universe, it provides a connected view of company activity within the broader private capital ecosystem.

You will join BlackRock’s Private Markets Data Engineering organisation, working on Preqin’s Company Intelligence platform at the core of our private markets data strategy. In this role, you will design, build, and operate scalable, production‑grade data platforms and pipelines that underpin the expansion of Preqin’s global private company universe, enabling broader coverage, deeper data, and more advanced analytics.

As a Vice President, you will operate as a player‑coach: a senior individual contributor providing hands‑on technical leadership while also managing and developing a small team of data engineers. You will own complex data engineering initiatives end‑to‑end, partner closely with product, analytics, and business stakeholders, and set a high bar for engineering excellence, reliability, and operational maturity in an enterprise environment.

• Provide senior technical leadership and architectural direction for data platforms and pipelines that enable the scalable expansion of Preqin’s private company universe, ensuring performance, cost efficiency, and long‑term maintainability.
• Own the end‑to‑end delivery of complex, business‑critical data engineering initiatives, from design through sustained production operation, with accountability for outcomes, reliability, and data trust.
• Drive a platform‑centric data engineering strategy, building reusable frameworks, shared datasets, and extensible pipelines that support multiple Company Intelligence products and advanced analytics use cases.
• Ensure high standards of data quality, governance, lineage, and observability, proactively managing operational and compliance risks across enterprise‑grade data products.
• Partner effectively with product, analytics, and business leaders, developing a deep understanding of strategic objectives and translating them into data platforms that deliver measurable business value.
• Lead, coach, and develop data engineers, combining hands‑on technical contribution with people management to foster a high‑performing, inclusive, and continuously improving engineering culture.

• Demonstrated experience (5+ years) operating enterprise‑scale, production‑grade data platforms, with a strong track record of reliability, performance, and stakeholder trust in business‑critical environments.
• Proven ability to exercise senior technical judgement, making architectural and prioritisation trade‑offs across complex data ecosystems, including scalability, cost, governance, and long‑term sustainability considerations.
• Advanced expertise in SQL and analytical data modelling, with sustained experience building high‑quality, well‑governed datasets used broadly across analytics, research, and decision‑making functions.
• Strong background in modern analytics engineering, including dbt‑based transformation layers, testing strategies, documentation, and embedded data quality practices in production environments.
• Proficiency in Python for building and operating data pipelines, orchestration, and automation, supported by solid software engineering fundamentals (version control, testing, CI/CD).
• Experience building and operating data pipelines using workflow orchestration frameworks (e.g. Apache Airflow), with a focus on reliability, observability, dependency management, and operational resilience.
• Experience designing and operating cloud‑native data platforms (AWS or Azure preferred) and enterprise data warehouses (Snowflake preferred), including performance tuning, cost controls, and secure access patterns.
• Experience leading, mentoring, and developing data engineers while maintaining hands‑on technical credibility.
   

• Experience working with private markets or alternative investment data (e.g. private equity, private credit, real assets, venture capital), supporting investment, research, or portfolio‑level analytics at scale.
• Exposure to advanced analytics or AI‑enabled capabilities embedded within data platforms, such as automation, model‑driven data products, or AI‑assisted workflows.
• Familiarity with modern data platform infrastructure and enablement practices, including infrastructure as code (Terraform or equivalent), container orchestration (Kubernetes), and related cloud‑native technologies.

• You operate with a strong sense of ownership and accountability, taking responsibility for complex, business‑critical data platforms and driving initiatives through to sustained production impact.
• You apply rigorous, analytical thinking to ambiguous problems, using evidence, engineering judgement, and structured decision‑making to deliver scalable, well‑governed solutions.
• You set a high bar for engineering excellence, embedding best practices in security, code quality, documentation, testing, and operational resilience.
• You collaborate effectively across disciplines, building trusted partnerships with product, analytics, and business stakeholders.
• You lead through influence and example, fostering an inclusive, collaborative, and high‑performing engineering culture.
• You demonstrate a growth mindset and continuous improvement orientation, embracing change and challenging established ways of working to deliver better outcomes at scale.