Engineering Manager, Data Platform

the company is building the bazaar for agentic systems. We're starting in capital markets, where our matching markets routinely account for more than 1% of notional U.S. equity market volume. By bringing together auction theory, distributed systems, artificial intelligence, and deep market structure expertise, we help humans and agentic systems transact more efficiently, improve how value is allocated, and, ultimately, increase global GDP.

We are seeking an Engineering Manager to build and lead our Data Engineering team. This role combines people leadership with real technical depth: you'll grow and support a team of engineers while helping shape the architecture of the pipelines, orchestration, and platform infrastructure that power our analytics. You'll be close enough to the code to make sound technical calls and review designs, and focused enough on the team to make sure everyone is set up to do their best work.

The ideal candidate has built and led data engineering teams in production environments and knows how to balance delivery, reliability, and long-term platform health. You'll partner closely with engineering leadership, research, and the business to set priorities and turn complex data needs into a clear, achievable roadmap.

• Design, build, and optimize the data pipelines, workflow orchestration, and internal tooling that power our analytics, writing production code alongside your team.
• Own the performance and correctness of data workflows end to end, including the parts that get interesting at 3am.
• Lead, mentor, and grow a team of data engineers, including hiring, onboarding, career development, and performance feedback.
• Own the roadmap for the data platform, balancing new capabilities, reliability work, and technical debt.
• Work closely with the engineers building our matching and auction systems so the data coming out of them is accurate, timely, and usable, where the edge cases are genuinely novel.
• Partner with research and business stakeholders to understand data needs and translate them into well-scoped projects.
• Improve reliability, fault tolerance, and observability across the data platform, and drive the on-call, incident response, and postmortem practices that keep it healthy.
• Set and uphold engineering standards for code quality, testing, and data integrity, and lead by example in design and code reviews.
• Work across the stack when needed, including infrastructure, Dev Ops, and the occasional deep-dive into why this one query is slower than it should be.
• Build a team culture that's technically rigorous, values ownership and clear communication, and expects people to push back.

• You have a Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent hands-on experience.
• You bring 8+ years of software or data engineering experience, including 2+ years directly managing engineers.
• You have a strong technical background Building safe and reliable data platforms, services and pipelines, primarily Python, SQL, Rust and Type Script.
• You've worked with workflow orchestration systems used to manage complex data pipelines (Temporal, Airflow, Dagster, or similar).
• You're familiar with columnar data formats such as Parquet or Arrow and with analytical databases built for large-scale workloads.
• You have experience building and running data systems in production.
• You've hired and developed engineers and enjoy helping people grow in their careers.
• You communicate clearly with both technical and non-technical partners and are comfortable owning prioritization tradeoffs.
• You have an entrepreneurial mindset and genuinely enjoy building things and solving hard problems.

• None of these are required, but they'll help you hit the ground running:
• Experience leading data teams at an exchange, ATS, broker-dealer, trading firm, or market data provider.
• Hands-on work with market data such as trades, quotes, order book events, or reference data, and an understanding of its quirks at volume.
• Familiarity with U.S. equity market structure and the order lifecycle, from submission through execution and reporting.
• Experience building pipelines for high-precision, event-ordered data, including nanosecond timestamps, late or out-of-order events, and point-in-time correctness.
• A track record of building data quality, reconciliation, and lineage practices where accuracy is non-negotiable.
• Exposure to data retention, auditability, and reporting requirements in a regulated environment (for example, SEC…