Data Engineering Manager

Founded in 1999, Geneva Trading is a premier global principal trading firm with strategically located offices in Chicago, Dublin, and London. Our relentless focus on trading excellence combined with technological innovation has equipped us with a best-in-class proprietary trading platform, enabling us to compete at the highest levels in the global markets. Rooted in a culture of integrity, collaboration, and an unwavering passion for progress, we foster an environment of personal and professional excellence. Our nimble organizational structure and entrepreneurial spirit attract top-tier talent with a passion for innovation, laying the foundation and driving our consistent success in the industry.

About Geneva Trading

Geneva Trading is a proprietary trading firm focused on high-frequency and algorithmic trading across global markets.

Execution matters, but it is only one part of the edge. A lot of what we do depends on the quality of the data behind the trading, research, analytics, monitoring, and post-trade workflows. If the data is late, wrong, incomplete, or hard to use, people feel it quickly.

This team owns that problem.

The Role

We are looking for a Data Engineering Manager to own our market data platforms and analytical data systems.

This is not a pure people-management role. You will manage a small team, but you will also be expected to write production code, review designs, debug systems, and stay close to the technical details. We are looking for someone who still wants to build and who can lead by being in the work with the team.

The core responsibility is to make sure our market data is captured, normalized, stored, and delivered correctly. The challenge is doing that across multiple venues, data sources, protocols, consumers, and performance requirements.

Trading systems, researchers, analysts, and monitoring tools all depend on this data. The person in this role needs to understand that reliability, correctness, and recoverability matter as much as speed.

New Opportunity

The way we use data is changing.

Historically, our market data platforms were built mainly for two types of consumers: trading systems that need fast and reliable access, and people doing research or analysis. We now have a third type of consumer emerging: AI-driven tools, models, and agents.

That changes some of the requirements. These systems need clean structure, good metadata, lineage, context, and access patterns that are not always the same as a human writing a query. They may search across data differently, ask questions differently, and generate query volumes that are very different from normal human usage.

We are not expecting someone to show up with all of this solved. We are also not looking for someone to simply bolt an AI interface onto an existing database. We want someone who understands where data platforms are going and can make practical engineering decisions now so the platform is ready for both human and machine-driven use.

Having a real point of view on this matters for the role.

What Success Looks Like

This is a deep stack, so we do not expect someone to master everything immediately. A rough first-year path would look like this:

In the first 90 days, you understand the main parts of the data stack, the people who depend on it, and the biggest pain points. You have shipped improvements to at least one real pipeline, not just reviewed documents or attended meetings.

By six months, you are helping steer the roadmap for market data infrastructure. You have improved reliability, performance, observability, or recoverability in a way we can measure. The team is relying on you in code reviews, design reviews, and production decisions.

By the end of the first year, you own the platform end to end, from ingestion through delivery. People across trading, research, and technology know to come to you for market data platform questions. You also have a clear view of how the platform needs to evolve as AI becomes a larger data consumer, and you have started moving it in that direction.

Key Responsibilities

Market Data Pipeline Engineering

Own the market data pipeline from ingestion through normalization and near-real-time delivery. The data has to be correct first, and the system has to recover cleanly when something breaks.

Responsibilities include:

Integrating direct exchange feed capture alongside third-party vendor data

Building and improving replay, recovery, and gap-detection capabilities

Keeping market data correctly sequenced, validated, and available fast enough for downstream users

Understanding when latency matters, when durability matters more, and how to make the right tradeoff

Time-Series Architecture: KDB+/Q

Design, maintain, and improve the KDB+/Q platforms that hold our real-time and historical market data.

Responsibilities include:

Schema design, partitioning, and query-performance tuning

Supporting real-time and historical analytics use cases

Managing retention and…