Job Description
Role Overview
As a Data Engineer, you will be part of the team delivering the data that enables a growing number of portfolio managers to research, test, execute, and manage investment strategies with ease and confidence, which is crucial to the firm’s success and growth.
You will source, analyze, clean, and curate vendor datasets, and integrate them seamlessly with other datasets in the Data Platform to increase their business value, including through platform effects. This will enable portfolio managers, Quants, and risk managers to focus on using high-quality data rather than wrestling with problematic raw data, increasing their productivity.
You have worked in or with Front Office businesses in the financial industry. You thrive on diving deep into data, understanding its business value, and improving datasets to increase that value. You are curious and take a meticulous, scientific approach to analyzing and testing data, ensuring the datasets you deliver add real value across the business.
Responsibilities
- Implement customer-centric data products: Collaborate with customers to understand their specific needs, then translate them into robust, scalable data solutions suited to the pace of hedge fund operations. Source, analyze, clean, and enrich relevant data; iterate with customers to increase its value; and work with the Data Platform team to maximize platform effects and firm-wide utility.
- Implement automated Data Quality checks: Build automated data quality checks across the development and data lifecycle to ensure data accuracy, reliability, quality, and robustness with high confidence. Automate monitoring and alerting so issues are addressed immediately with minimal business impact.
- Integrate with vendor & platform systems: Manage data feed integration with external vendors, focusing on fast onboarding and rigorous data validation, as well as integration with internal third-party trade, position-keeping, risk systems, and the firm’s central Data Platform.
- Contribute to platform development: Identify recurring customer needs and propose and implement configurable frameworks. Ensure sufficient process telemetry to detect bottlenecks and issues and support high availability, robustness, and performance.
- Role-model continuous improvement: Maintain high standards of technical and analytical excellence, ownership, and customer care. Mentor junior analysts, support hiring, and streamline workflows to improve data quality, efficiency, and decision-making.
Qualifications & Experience
- BSc/MSc/PhD in Computer Science, Physics, Engineering or similar and 4+ years financial industry experience in Front Office / Quant / organizations or on a PM desk, preferably with some time spent in a hedge fund.
- Technical
- Advanced data analysis skills in Python, with familiarity with Pandas, Polars, and/or Snowpark dataframes. Experience with high-throughput, low-latency programming in C#, F#, C++, or Java is a plus.
- Advanced SQL skills and experience with modern data storage and querying technologies such as Snowflake, Redshift, and BigQuery, as well as file formats such as Parquet and Iceberg.
- Hands-on experience with cloud platforms such as AWS, Google Cloud, or Azure, and related data storage and processing services such as AWS RDS (Postgres), S3, and MSK (Kafka).
- Familiarity with Linux environments, Git, and modern DevOps practices.
- Demonstrated experience in test automation to maintain high standards and support rapid change with confidence, preferably including DBT.
- Familiarity with monitoring production systems using modern observability and alerting solutions such as Grafana/Prometheus, Datadog, or ELK is desirable.
- Hands-on experience with data pipeline orchestration tools such as Airflow, and data download mechanisms such as SFTP and vendor APIs.
- Proficiency in integrating with third-party data providers and vendor APIs commonly used in hedge funds would be ideal.
- Financial Data: Familiarity with at least some market and reference data, ideally across a broad range of asset classes, hedge fund data workflows (sourcing, processing, analysis), real-time data needs, and financial compliance considerations (e.g. licensing, access control). Clear understanding of how data adds value to a hedge fund’s business, and how that value can be increased.
- Soft Skills: Effective communication skills with Front Office stakeholders and Tech colleagues, curiosity, a scientific and collaborative mindset, ability to produce in an agile environment, and drive to complete projects independently.