Data engineer in private markets

Описание:

Preqin, part of Black Rock, provides private markets data, benchmarks, and analytics covering private equity, private credit, real estate, infrastructure, venture capital, and hedge funds. Its Company Intelligence platform delivers structured company, deal, ownership, fund, manager, and performance data to support investment analysis, due diligence, valuation benchmarking, and research.

• Provide senior technical leadership and architectural direction for scalable data platforms and pipelines supporting Preqin's private company universe;
• Own end-to-end delivery of complex, business-critical data engineering initiatives from design through sustained production operation;
• Drive a platform-centric data engineering strategy through reusable frameworks, shared datasets, and extensible pipelines;
• Ensure data quality, governance, lineage, and observability while managing operational and compliance risks;
• Partner with product, analytics, and business leaders to translate strategic objectives into data platforms with measurable business value;
• Lead, coach, and develop data engineers while contributing hands-on technically;
• Operate complex, business-critical data platforms and drive initiatives through sustained production impact;
• Embed best practices in security, code quality, documentation, testing, and operational resilience;
• Build trusted partnerships across product, analytics, and business functions;
• Foster an inclusive, collaborative, and high-performing engineering culture;
• Drive continuous improvement and challenge established ways of working.

• 5+ Years of experience operating enterprise-scale, production-grade data platforms;
• Experience making architectural and prioritisation trade-offs across complex data ecosystems;
• Advanced SQL and analytical data modelling expertise;
• Experience building governed datasets for analytics, research, and decision-making;
• Strong background in modern analytics engineering, including dbt, testing, documentation, and data quality practices;
• Proficiency in Python for data pipelines, orchestration, and automation;
• Solid software engineering fundamentals, including version control, testing, and CI/CD;
• Experience with workflow orchestration frameworks such as Apache Airflow;
• Experience with cloud-native data platforms, preferably AWS or Azure;
• Experience with enterprise data warehouses, preferably Snowflake;
• Experience with performance tuning, cost controls, and secure access patterns;
• Experience leading, mentoring, and developing data engineers;
• Strong ownership, accountability, analytical thinking, and structured decision-making;
• Nice to have: private markets or alternative investment data experience, advanced analytics or AI-enabled data platform capabilities, Terraform or equivalent, Kubernetes, related cloud-native technologies.

• Retirement investment and financial planning tools
• Education reimbursement
• Physical health and emotional well-being resources
• Family support programs
• Flexible Time Off (FTO)
• Employees are required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week.