About the Organization
This organization is a global investment management firm focused on alternative strategies across credit, equity, and real assets. With a long-term, risk-aware investment philosophy, it operates at significant scale across multiple regions and supports a large, diverse workforce worldwide.
The culture emphasizes collaboration, intellectual curiosity, and inclusion, with a strong commitment to professional growth and community engagement. Ongoing learning, internal mobility, and meaningful philanthropic efforts are core to how teams operate and grow together.
The data function plays a central role in enabling investment and business decision-making by delivering accurate, well-governed, and accessible data. By partnering closely with technology and business teams, the group drives enterprise data architecture, platform development, and governance to support analytics, reporting, and data-driven products.
Role Overview
As the Head of Data Platform Engineering, you will be responsible for the technical direction, execution, and continued advancement of enterprise-wide data platforms. This role leads teams that design, build, and run both modern cloud-based data systems and transitioning legacy environments that support critical investment, operational, and reporting processes.
You will guide large-scale platform initiatives while ensuring day-to-day reliability and disciplined delivery. Success in this role requires the ability to modernize infrastructure without disrupting essential business workflows and to align engineering decisions with broader technology and data strategy.
You will collaborate closely with teams spanning analytics engineering, data operations, governance, data products, and core technology to ensure the data ecosystem is trusted, scalable, and positioned for long-term success.
Key Responsibilities
Data Platform Strategy and Architecture
- Set the technical vision and oversee the design and evolution of enterprise data platforms
- Lead modernization efforts, including migration away from legacy environments while maintaining stability for critical use cases
- Ensure data platforms support analytics, reporting, and product development at scale
- Define and enforce architectural standards, engineering principles, and platform guardrails across the data ecosystem
- Partner with enterprise architecture and technology teams to establish secure, scalable, and resilient platform patterns
Engineering Execution and Delivery
- Own delivery across the data platform organization, from roadmap definition through production rollout
- Establish development practices that emphasize reliability, scalability, and maintainability
- Track and improve delivery outcomes using metrics such as throughput, platform adoption, stability, and cost efficiency
- Bring hands-on expertise with cloud-native data stacks, including platforms such as Azure, AWS, Snowflake, and Databricks, and pipeline development using tools like dbt, Python, and related ETL technologies
- Align platform capabilities with enterprise priorities through close partnership with data product and business stakeholders
Reliability and Operational Readiness
- Design platforms with strong operational foundations, including monitoring, alerting, and support readiness
- Define standards for incident response, runbooks, and operational ownership
- Work closely with data operations teams responsible for ongoing monitoring and support
- Drive continuous improvement through post-incident reviews and platform performance analysis
People Leadership and Team Development
- Define operating models, engineering standards, and development practices for platform teams
- Lead and mentor managers and engineers responsible for enterprise data platforms
- Guide teams through the transition from legacy systems to cloud-native architectures
- Foster a culture centered on accountability, collaboration, and continuous improvement
Required Experience and Skills
- Fifteen or more years of experience in data engineering, platform engineering, or enterprise data infrastructure
- Extensive experience leading teams that design, build, and operate large-scale data platforms
- Deep familiarity with modern data architectures, including data warehouses, data lakes, and cloud-based ecosystems
- Proven success modernizing legacy data systems and leading complex migration efforts
- Strong understanding of data pipelines, orchestration frameworks, architectural patterns, and data modeling
- Experience implementing disciplined delivery practices such as CI/CD, automated testing, and production monitoring
- Track record of defining and using engineering metrics to measure reliability, delivery effectiveness, and platform usage
- Experience managing vendors, consultants, and strategic technology partners
- Ability to communicate effectively with both technical teams and business leaders
- Prior experience in financial services or asset management environments is preferred
- Deep technical knowledge across modern enterprise data stacks, including lakehouse technologies, orchestration tools, observability, data quality, metadata management, and DevOps automation
- Sound judgment when making build-versus-buy decisions and standardizing platform technologies
Education
- Bachelor’s degree in computer science, engineering, information systems, or a related field
