About the Opportunity
Our client, a global investment management firm, is seeking a Data Engineer to join their data and technology team. This position offers the chance to work on a state-of-the-art market data platform and deliver solutions for investment, analytics, and business teams in a fast-paced, high-impact environment. The role sits in New York City and is currently operating on a hybrid schedule – 4 days in office, and 1 day remote.
The annual base salary range is $140,000 to $165,000. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things. Details about eligibility for bonus compensation (if applicable) will be finalized at the time of offer.
Job Responsibilities
- Design, build, and maintain robust data pipelines and tools for real-time, unstructured, and alternative data sets
- Develop automation solutions to increase efficiency, quality, and resilience of the data platform
- Build and support tools for onboarding, analysis, validation, and lifecycle management of financial data
- Partner with Product and Portfolio Managers to deliver end-to-end data solutions tailored to their requirements
- Work with timeseries databases (e.g. ArcticDB, KDB+) to store, query, and manage dense time-series data efficiently
- Create and implement data quality and validation tools to ensure data integrity and robust error handling
- Identify, investigate, and resolve data issues; liaise with data providers and business users for seamless data flows
Job Requirements
- Bachelor’s degree in Computer Science, Engineering, or a related field
- 4–6 years of hands-on experience in software development or data engineering
- Proficient in Python and SQL
- Excellent communication and collaboration skills; able to work with a cross-functional data and investment team
Preferred Skills
- Knowledge of fixed income and financial products (options, swaps, futures, etc.)
- Familiarity with AWS ecosystem and cloud-based data infrastructure
- Experience with streaming pipelines and OLAP platforms (Snowflake, Trino)
- Awareness of key financial vendors and data providers (Bloomberg, Refinitiv, FactSet)
- Experience with timeseries databases (ArcticDB, KDB+)
- Background working collaboratively with trading, data, and infrastructure teams
