Engineering Manager

About the role

PitchBook — a platform for investment professionals. Our software provides access to data and the analytical tools to get answers fast and discover promising opportunities. Uncovers actionable insights and trends hidden within the financial data of millions of companies. Users all over the world include large corporations, start-ups, venture capital and private equity firms, investment banks, and many others.

Data Collection Technology is a core technology domain responsible for ingesting, processing, managing, and publishing data to the main platform. It combines a suite of battle-proven legacy data collection tools with a modern data collection platform.
The broader data management platform provides a unified environment for managing and validating data across both private and public markets. It brings together multiple tools and workflows, standardizes quality-assurance processes, and enables scalable automation for data operations.

As an Engineering Manager in PitchBook’s DataTech team, you will be responsible for ensuring your teams execute well and deliver the outcomes they commit to. Your primary focus will be strengthening delivery capabilities and helping teams improve planning, execution and predictability.

You will also ensure effective platform-wide operational processes, including customer support and incident management. People management and team development remain important responsibilities, with successful and sustainable delivery as the primary focus.

Tech Stack: Java 21, Kubernetes/GitOps, Spring Boot, MFE Architecture, OpenAPI, PostgreSQL, Kafka, Redis

Team setup: 2 teams

Work Environment: ​​flexible

As a qualified expert You will

Own delivery and engineering outcomes for DataTech teams

• Be accountable for delivery, velocity, quality, reliability and scalability
• Ensure work is aligned with DataTech priorities, using effective visualization and reporting to understand delivery, identify problems and drive improvements

Ensure strong SDLC execution and technical quality

• Ensure teams execute effectively across refinement, design, estimation, implementation, testing and deployment
• Identify execution gaps and work with SDE Leads to improve how delivery works
• Invest in improving a AI-enabled SDLC, including adoption and evolution of SDD frameworks and practices

Plan and manage capacity

• Own short- and long-term capacity and capability planning
• Allocate people in line with DataTech priorities and delivery commitments
• Surface capacity and delivery risks and drive mitigation

Own product quality, reliability and operations

• Be accountable for stability, reliability, performance and overall quality
• Ensure effective platform-wide customer support, incident management, observability and other operational processes
• Balance delivery with technical health and operational needs

Lead and grow engineers and SDE Leads

• Manage engineers and SDE Leads through coaching, feedback, growth and performance management
• Build and evolve teams through hiring, onboarding and shaping the right skill mix
• Maintain healthy team dynamics and address people issues early

Strengthen ways of working and collaboration

• Improve engineering practices where they help teams deliver more effectively
• Partner with Product, Data and other domains to align plans and manage dependencies
• Contribute to domain-level planning and cross-team collaboration