Data Engineer
Who we are:
Pharo Management is a leading global macro hedge fund with a focus on emerging markets. Founded in 2000, the firm has offices in London, New York, Hong Kong and Abu Dhabi, and currently manages $8 billion in assets across four funds. Pharo trades foreign exchange, sovereign and corporate credit, local market interest rates, commodities, and their derivatives. We trade in over 70 countries across Asia, Central and Eastern Europe, the Middle East and Africa, Latin America as well as developed markets. Our investment approach combines macroeconomic fundamental research and quantitative analysis.
Pharo employs a diverse, dynamic team of 140 professionals representing over 20 nationalities and 30 languages. We have a strong corporate culture anchored in core values such as collaborative spirit, creativity, and respect. We are passionate about what we do and are committed to attracting the best and brightest talent.
This is a great opportunity to join a market leader and contribute to our continued success.
Job description:
We are looking for a strong Data Engineer to help modernize our core data and API platforms in our New York office. Working closely within our collaborative data team, you will bridge traditional quantitative financial systems and modern cloud infrastructure building robust pipelines, for handling complex market data analytics, supporting both high-performance trading platforms and emerging AI data workflows.
Responsibilities:
- Modernize legacy workflows into modern ELT/orchestration; design, build, and maintain production-grade pipelines (SQL, Python, Snowflake, dbt) moving data from on-prem systems (e.g., SQL Server), vendor feeds, APIs, and flat files into the cloud.
- Ingest, structure, and optimize complex financial datasets specializing in high-precision pricing analytics, yield curves, volatility surfaces, risk metrics, and P&L attribution.
- Diagnose slow queries and bottlenecks; implement indexing, clustering, caching, and pre-aggregation for Power BI and internal tools. Expand Python-based API infrastructure to scale dataset access across the firm.
- Build robust data pipelines for unstructured document storage, parsing, and ingestion. Develop and maintain semantic layers and structured feature sets tailored for AI applications
- Implement automated checks for missing/stale data, schema drift, duplicates, and outliers. Build dashboards for market data anomalies and maintain a user-facing data catalog (datasets, lineage, ownership, and quality rules).
- Balance modernization initiatives with day-to-day troubleshooting, user support, and vendor follow-ups.
Required qualities and skills:
- 5-7 years in data engineering, analytics engineering, or quantitative data roles, ideally within a hedge fund, asset manager, or systematic trading shop.
- Advanced SQL (performance tuning, complex transforms, dimensional modeling) and hands-on Python (Pandas, Polars, and API integrations).
- Experience with Microsoft SQL Server, a modern cloud data warehouse (Snowflake preferred), and transformation tools like dbt. Familiarity with AI toolset in cloud platform.
- Strong, hands-on knowledge of quantitative financial concepts specifically deep familiarity with Fixed Income, yield curves, fx, volatility datasets, trade lifecycles, and reference data feeds (e.g., Bloomberg, LSEG).
- Clear communicator comfortable partnering with technical and business stakeholders alike. A can-do attitude for tasks both big and small.
Desirable skills:
- Experience with high-frequency tick databases or specialized analytical engines (e.g., Azure Data Explorer/KQL).
- Exposure to streaming or event-driven patterns (Event Hubs or Change Data Capture/CDC).
- BI tool optimization experience (Power BI or Tableau) and workflow orchestrators.
- Experience building semantic layer
Work status and location:
Pay range in New York:
- Exact compensation may vary based on skills, experience, and location.
- Base salary - $150,000-180,000/year
