Director of Data Engineering

Overview of 73 Strings:

73 Strings is a global FinTech powerhouse transforming the analysis, valuation, and monitoring of illiquid assets. Our cutting-edge, AI-driven platform streamlines data extraction, standardization, and fair value estimation, empowering asset managers and investment professionals to achieve unparalleled clarity and efficiency. We serve a diverse, global clientele, from major private equity firms to dynamic growth funds by delivering insights with exceptional speed and accuracy. In February 2025, we successfully closed a $55 million Series B funding round led by Growth Equity at Goldman Sachs Alternatives, with participation from Blackstone Innovations Investments, Golub Capital, Hamilton Lane, and Broadhaven Ventures.

About the Role

We are seeking a highly experienced Director of Data to lead our end-to-end data organisation, spanning data engineering, data science, machine learning, analytics, and data architecture. This is a strategic and hands-on leadership role responsible for defining the company’s data vision, building scalable and resilient data platforms, driving AI-led innovation, and enabling high-quality insights and data integrations for both internal teams and external partners.

The ideal candidate brings deep expertise in data architecture, modern cloud data ecosystems, and experience leading advanced business intelligence and ML teams. You will partner closely with Software engineering, AI engineering, Product, and business stakeholders to drive data maturity, improve decision-making, and support our product roadmap.

Experience working in SaaS, financial services, or ideally both, is essential.

Key Responsibilities

Data Strategy & Leadership

·       Define and execute the company’s multi-year data strategy, roadmap, and target data architecture.

·       Build, scale, and lead high-performing teams across data science, ML engineering, data engineering, analytics, and data governance.

·       Establish organisation-wide standards for data quality, consistency, access, and governance.

·       Work with product and engineering to enable data driven product decisions and build end-to-end data platform

Data Architecture & Platform Ownership

·       Architect scalable, secure, and high-performance data platforms and pipelines.

·       Oversee ingestion, modelling, transformation, and orchestration of structured and unstructured data.

·       Ensure modern data stack best practices (e.g., warehouse/lakehouse, orchestration, feature stores, catalogues, ML pipelines).

·       Optimize and transform data for low latency delivery to client facing applications (web, Excel etc.).

Data Science & Machine Learning

·       Lead development, deployment, monitoring, and lifecycle management of ML models.

·       Partner with product teams to embed ML features into customer-facing products.

·       Enable AI innovation with modern data and machine learning infrastructure.

Analytics & Insights

·       Oversee creation of dashboards, metrics frameworks, and analytical tools that empower teams across the business.

·       Ensure analytics output is accurate, trustworthy, and tied to business outcomes.

·       Partner with finance, sales, and leadership on forecasting, performance analysis, and strategic planning.

 Data Governance & Compliance

·       Implement data governance practices including access control, metadata management, and lineage.

·       Ensure compliance with relevant regulatory frameworks (GDPR, CCPA, SOC 2, etc.).

·       Drive data quality programs with automated monitoring and remediation processes.

Qualifications

Required

·       Proven experience building, leading, and scaling multi-disciplinary data teams of 10+ members across data engineering, analytics, and machine learning functions.

·       Deep expertise in modern data architecture, cloud data platforms, and scalable pipelines.

·       Proven leadership experience managing multi-disciplinary data teams.

·       Demonstrated success within innovative, product-led technology companies, ideally operating in data-intensive or regulated domains.

·       Experience implementing data governance, data quality frameworks, and operational data processes.

·       Strong business acumen with ability to translate complex data into strategic, actionable insights.

·       Excellent communication skills and ability to influence senior leadership.

Preferred

·       Prior experience in high-growth, fast-moving technology environments.

·       Exposure to alternative investments, capital markets, risk modelling, or financial datasets.

·       Experience building or supporting ML-enabled SaaS products.

·       Deep expertise in designing and scaling modern, cloud-native data platforms that support analytics, machine learning, and data products in production.

·       Strong architectural judgment, with a pragmatic approach to trade-offs across scalability, reliability, cost, and governance.

What Success Looks Like 

·       A robust, scalable, and future-ready data platform that supports analytics, ML, and operational systems.

·       Clear data governance frameworks that enable trust and compliance.

·       High-performing, motivated teams that deliver with speed and quality.

·       Tangible business impact through data-driven insights and ML-powered features.

·       Strong cross-functional alignment and adoption of data best practices.