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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineering Manager - **Company:** Geneva Trading - **Location:** Chicago, IL, United States - **Experience:** Experienced - **Salary:** $180,000.0 - $250,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Algorithmic Trading, Data Analysis, C++ (Programming Language), Code Review, Encodings, Computer Programming, Databases, Continuous Integration, Data Distribution Service, Information Engineering, Data Infrastructure, Software Debugging, Linux, Monitoring of Systems, High-Frequency Trading, Intrusion Detection and Prevention, Python (Programming Language), Metadata, Multicasting, Packet Analyzer, Performance Tuning, Query Optimization, Reference Data, Message Oriented Middleware, Data Streaming, Systems Integration, Tcpdump, Git, KDB+, Low Latency, Production Code, Enterprise Integration, Data Management, Tools for Reporting, Data Pipelines, Docker, Service Stack - **Published:** July 17, 2026 - **Apply:** https://www.dice.com/job-detail/03495727-3409-4423-9a05-edc3f5435642 ## About the Role * 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, * 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 ## Description 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., 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. 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) ## Related Videos - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [Docker exec without Docker](https://www.wearedevelopers.com/videos/1094-docker-exec-without-docker) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) ## Related Articles - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [Data Analyst Salary in Switzerland](https://www.wearedevelopers.com/magazine/276-data-analyst-salary-in-switzerland) - [Top-Paying Tech Jobs (with Salaries)](https://www.wearedevelopers.com/magazine/372-top-paying-tech-jobs-with-salaries)