Data Engineer (KDB+ / Q)

Job Title: Data Engineer (KDB+ / Q) Job Type: Contract (W2) Contract Duration: ASAP through 12/31/2025 (with potential for extension) Work Schedule: Monday-Friday, 8 hours per day, 40 hours per week (standard business hours) Location: New York, NY (onsite at end-client's office 4 days per week, with potential for 5 days) Compensation: $90 to $100 per hour Overview: A Big Four consulting firm is seeking an experienced Data Engineer with deep expertise in KDB+ and Q to support a high-performance financial services engagement. This role requires an individual with hands-on experience designing, implementing, and optimizing KDB+ time-series databases and developing in Q to enable ultra-low latency data processing. The successful candidate will work directly with client stakeholders in a front-office environment, supporting data-driven initiatives that demand millisecond-level efficiency. Responsibilities: • Design, develop, and maintain KDB+ time-series databases to support real-time and historical data analysis. • Write and optimize Q scripts for data ingestion, transformation, querying, and analytics. • Collaborate with quantitative analysts, traders, and technology teams to implement data solutions that enable high-performance trading and risk management. • Ensure database scalability, stability, and performance tuning for ultra-low latency applications. • Build robust data pipelines for ingesting large volumes of structured and unstructured market and transactional data. • Troubleshoot production issues and provide ongoing support for mission-critical systems. • Document system designs, processes, and technical specifications. Required Qualifications: • High school diploma (or GED/equivalent). • 5+ years of professional experience as a Data Engineer or similar role. • Strong hands-on expertise in KDB+ and Q programming language. • Proven experience with time-series databases in trading, quantitative research, or financial services. • Solid understanding of market data, order flows, and real-time financial systems. • Ability to work in a fast-paced environment with tight deadlines. • Excellent communication and collaboration skills, with prior experience working onsite with clients. Preferred Qualifications: • A bachelor's (or advanced) college degree. • Experience with data engineering frameworks (e.g., Python, Spark, Kafka). • Knowledge of cloud platforms (AWS, GCP, or Azure) for large-scale data processing. • Background in quantitative finance or algorithmic trading support.