KDB Development Lead

Millennium•New York, NY

4d•$175,000 - $250,000•Onsite

About The Position

Information Technology is central to the health and growth of Millennium’s active, multi-manager business model, which requires flexible, scalable technology and advanced proprietary systems. The KDB Development team builds and operates high-performance time-series data platforms using kdb+ and q, with a focus on the architecture, performance, reliability, and usability of real-time and historical databases. The team also supports accessible analytics interfaces, including Jupyter notebooks, and partners closely with trading, research, data, and infrastructure stakeholders.

Requirements

Significant experience developing and operating production systems using kdb+ and q, including the design, support, and tuning of real-time and historical database architectures.
Expertise in real-time data ingestion, tickerplant-based or comparable streaming architectures, intraday recovery, end-of-day processing, and historical data management.
Strong command of time-series data design, high-performance q queries, partitioned databases, memory management, and operational performance troubleshooting.
Experience delivering analytical workflows through Jupyter notebooks, including q/Python interoperability and well-governed self-service data access.
Demonstrated technical leadership and experience guiding engineers through delivery as a people manager, team lead, or senior technical lead.
Strong software engineering discipline, including testing, code review, CI/CD, version control, and production release practices.
Experience integrating KDB with adjacent technologies such as Python, Java, C++, APIs, messaging platforms including Solace or Kafka, and infrastructure tooling.
Clear written and verbal communication skills, with the ability to partner effectively with technical and non-technical stakeholders in high-throughput, latency-sensitive environments.

Responsibilities

Lead, mentor, and develop a team of KDB/q engineers, establishing clear priorities, engineering standards, accountability, and ownership.
Own the architecture and roadmap for kdb+ real-time and historical database platforms, including data ingestion, intraday processing, end-of-day workflows, storage, retention, recovery, and query access.
Design and implement scalable q solutions for high-volume real-time and historical time-series data, with a focus on performance, reliability, and maintainability.
Drive performance tuning across memory management, database partitioning and sym attributes, query patterns, indexing, process communication, and capacity planning.
Build and maintain analyst-facing data-access tooling, including Jupyter notebook environments, q/Python integrations, reusable notebooks, and supported query libraries.
Establish engineering best practices for code quality, automated testing, source control, CI/CD, release management, documentation, monitoring, and incident response.
Embed data-quality controls, reconciliation processes, entitlements, auditability, and retention policies into platform design.
Partner with trading, quantitative research, operations, data engineering, and infrastructure teams to deliver durable solutions; lead design and code reviews, production support, and continuous improvements following incidents.