Job description

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 data lifecycle policies Keeping the platform maintainable as data volumes and usage grow Debugging production HDB and tickerplant issues directly Data Distribution & Platform Integration Deliver data reliably to downstream consumers through streaming, messaging, and platform integrations. Responsibilities include: Defining data contracts and schemas that other teams can depend on Supporting replayable and durable data flows where needed Working with downstream teams to understand how they actually consume the data Balancing real-time delivery needs with reliability and operational simplicity Tooling, Libraries & Supporting Systems Build the internal tooling and shared libraries that make the data platform easier to operate and easier to use. Responsibilities include: Building validation, monitoring, replay, and analytics tools Owning supporting systems for reference data, configuration, and metadata Improving developer workflows around market data testing and troubleshooting Reducing repeated manual work through better tools and automation Technical Leadership & Production Ownership Lead the team by staying close to the work. Responsibilities include: Writing production code Reviewing pull requests and technical designs Working directly with trading and research teams to understand their needs Debugging production issues during market hours when needed Setting expectations for quality, reliability, and maintainability Improving monitoring, alerting, and data-quality checks so problems are caught before the desk finds them Technology Stack KDB+ / Q Python C / C++ Linux Docker Git / CI-CD Binary market data protocols Streaming / message bus platforms Kernel-bypass / high-performance networking Industry-standard messaging protocols (FIX, SBE) Required Qualifications At least 7 years of experience in data engineering, market data infrastructure, or a closely related area Current hands-on production coding experience At least 3 years leading engineers while staying technically involved Strong KDB+/Q experience, including complex Q, tick architecture, query tuning, and production HDB troubleshooting Strong production Python experience, including tested, packaged, maintainable systems-level code Experience building low-latency decoders for real exchange protocols Strong understanding of multicast, packet capture, sequencing, and gap detection Comfortable working in Linux and using tools such as perf, strace, tcpdump, and numactl Able to own production issues directly, not just route them to someone else Preferred Qualifications Background in high-frequency trading, market making, proprietary trading, or another latency-sensitive environment C or C++ experience for performance-critical decoder or capture components Experience with kernel-bypass or high-performance networking technologies Experience with streaming platforms used in real-time data pipelines Working knowledge of binary market data encoding standards Contributions to open-source data tooling, market data systems, or quantitative research infrastructure Role Details Reports to the CTO Based in Chicago, IL; hybrid, with three days per week in the office No direct on-call rotation; the platform has 24-hour coverage from dedicated staff and support teams Compensation Base Salary Range: $180,000 - $250,000, plus eligibility for a performance-based bonus. Final compensation will be determined based on the candidate’s skills, experience, education, and qualifications. In addition to base salary, Geneva Trading offers a competitive total rewards package, including a comprehensive benefits program. Learn more about our employee incentives here: genevatrading.com Application expected to close: 9/01/2026 We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Requirements

Must-haves

We are looking for a Data Engineering Manager to own our market data platforms and analytical data systems.
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.
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:
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.
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.
Schema design, partitioning, and query-performance tuning
Supporting real-time and historical analytics use cases
Managing retention and data lifecycle policies
Keeping the platform maintainable as data volumes and usage grow
Debugging production HDB and tickerplant issues directly
Data Distribution & Platform Integration
Deliver data reliably to downstream consumers through streaming, messaging, and platform integrations.
Defining data contracts and schemas that other teams can depend on
Supporting replayable and durable data flows where needed

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythondocker

Platform: greenhouse
Effort: quick