Data Engineer (Credit Technology) | Multi-Strat Fund
About the Role
You'll design and develop modern data infrastructure and applications, enabling portfolio managers and researchers to access high-quality, scalable data solutions. The role involves working across ingestion, ETL, data quality, storage, and APIs, while collaborating closely with quantitative engineers, researchers, and technical teams in a global environment.
Key Responsibilities
- Build and optimize data pipelines and platforms for large-scale, complex datasets.
- Design and implement robust ETL processes and data models for batch and streaming workflows.
- Collaborate with quants and engineers to deliver scalable solutions aligned with business needs.
- Develop APIs and tools to enable efficient data consumption and integration.
- Ensure performance, reliability, and scalability across distributed systems.
Ideal Candidate Profile
- 5+ years of experience in data engineering or similar data-intensive roles.
- Strong Python skills and experience with data libraries (e.g., Pandas, Polars, Dask, PySpark).
- Expertise in SQL and relational databases; familiarity with columnar formats (Parquet/Arrow).
- Experience with distributed systems and messaging tools (e.g., Kafka, Redis).
- Knowledge of data lake/warehouse architectures and performance tuning.
- Excellent problem-solving and communication skills.
- Bonus: Experience with REST APIs, monitoring tools (Prometheus, Grafana), and financial datasets.
If you feel the role is a good fit - apply today!
FAQs
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