Lead KDB Engineer

A financial firm is looking for a Lead KDB Engineer to join their team in New York, NY. 

Compensation: $200k base bonus 

Responsibilities:

• Design, develop, and maintain kdb databases and risk engine components.
• Develop solutions in Q/kdb , Python/PyKX to enhance market risk data processing.
• Implement scalable and high-performance computing solutions for market risk analytics.
• Collaborate with cross-functional teams and global counterparts to deliver high-quality solutions.
• Optimize risk data processing pipelines to improve efficiency and response time.
• Ensure compliance with regulatory requirements for market risk technology.
• Work in an Agile development environment, ensuring timely and efficient product delivery.
• Provide technical leadership and mentorship to junior developers in the squad.
• Troubleshoot and resolve performance issues within KDB and risk analytics platforms.

Qualifications:
Required

• Hands-on experience with kdb /Q.
• Proficiency in Python and PyKX for data analytics and processing.
• Experience in designing and building large-scale business-critical systems.
• Strong fundamentals in data structures and algorithms.
• Ability to understand market risk domain and its data and implement efficient data solutions.
• Strong problem-solving and analytical thinking.
• Ability to act autonomously in complex decision-making.
• Capacity to develop and manage operational initiatives that align with business goals.
• Good communication skills.
• Must be comfortable and effective working independently (team is global - mostly in Budapest, with some presence in NY & Montreal).
• 5 years of designing and building apps - building distributed systems is a plus.

Preferred

• Experience with Java, kdb Insights a plus.
• Architectural understanding of kdb and distributed computing.
• Exposure to big data technologies such as Apache Spark.
• Familiarity with Agile software development and DevOps best practices.
• Experience with cloud technologies such as AWS, Azure, or Google Cloud Platform (GCP).
• Knowledge of cloud-based data processing frameworks and containerized deployments (Docker, Kubernetes).
• Experience with Financial technologies (FinTech) or background in finance.