Data Engineering Manager

Geneva Trading•Chicago, IL

20h•$180,000 - $250,000•Hybrid

About The Position

Geneva Trading is a premier global principal trading firm with offices in Chicago, Dublin, and London. They specialize in high-frequency and algorithmic trading. The company emphasizes the critical role of high-quality data in trading, research, analytics, monitoring, and post-trade workflows. This role is responsible for owning and managing the market data platforms and analytical data systems, ensuring data is captured, normalized, stored, and delivered correctly across multiple venues, data sources, protocols, and performance requirements. The position requires a blend of technical leadership and hands-on coding, with a focus on reliability, correctness, and recoverability. The role also involves adapting data platforms for emerging AI-driven tools and models, requiring a forward-thinking approach to data engineering.

Requirements

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.

Nice To Haves

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.

Responsibilities

Own the market data pipeline from ingestion through normalization and near-real-time delivery, ensuring data correctness and system recoverability.
Integrate direct exchange feed capture alongside third-party vendor data.
Build and improve replay, recovery, and gap-detection capabilities.
Ensure market data is correctly sequenced, validated, and available with sufficient speed for downstream users.
Understand and balance the trade-offs between latency and durability.
Design, maintain, and improve KDB+/Q platforms for real-time and historical market data.
Manage schema design, partitioning, and query-performance tuning for KDB+/Q.
Support real-time and historical analytics use cases on KDB+/Q.
Manage retention and data lifecycle policies for KDB+/Q.
Debug production KDB+ HDB and tickerplant issues directly.
Deliver data reliably to downstream consumers via streaming, messaging, and platform integrations.
Define data contracts and schemas for inter-team data dependencies.
Support replayable and durable data flows.
Collaborate with downstream teams to understand their data consumption patterns.
Build internal tooling and shared libraries for data platform operation and usability.
Develop validation, monitoring, replay, and analytics tools.
Own supporting systems for reference data, configuration, and metadata.
Improve developer workflows for market data testing and troubleshooting.
Reduce manual work through automation and better tools.
Lead the team by staying technically involved and close to the work.
Write production code.
Review pull requests and technical designs.
Collaborate with trading and research teams to understand their requirements.
Debug production issues during market hours as needed.
Set standards for quality, reliability, and maintainability.
Enhance monitoring, alerting, and data-quality checks to proactively identify issues.