We're rebuilding our technology and data platform from the ground up-and we're looking for superstar Data Engineers to help lead the change. If you're a curious, creative problem solver with 4-10 years of hands-on experience, this is your chance to shape the pipelines that power everything from multi-asset investment strategies to AI-driven insights.
You'll architect the data backbone that fuels our research, risk systems, and execution workflows. If you thrive in high-performance environments and love turning raw data into reliable, scalable infrastructure, we want to hear from you.
Responsibilities:
- End-to-End Pipelines: Design, build, and optimize scalable data pipelines for structured and unstructured financial datasets.
- Core Data Delivery: Collaborate with discretionary PMs to deliver timely, trustworthy data for decision-making.
- Quant-Ready Infrastructure: Partner with systematic teams to shape data for advanced analytics and modelling.
- API Innovation: Help evolve our data delivery platform with user-friendly APIs accessible via Excel, Python, and more.
- Quality at Scale: Implement monitoring, validation, and remediation frameworks to ensure data accuracy and consistency.
- Governance Advocacy: Champion standards, lineage, metadata, and security across the data ecosystem.
- Automation & Efficiency: Drive ETL/ELT automation using cloud-native and distributed systems.
- Vendor Integration: Work with internal and external data providers to onboard and manage critical data assets.
Qualifications
- A Bachelor's or Master's in Computer Science, Engineering, or a related field.
- Strong coding skills in Python and SQL, plus experience with distributed systems like Spark, Kafka, or Hadoop.
- Deep knowledge of lakehouse architectures and open table formats (Iceberg, Delta Lake, Parquet).
- Hands-on experience with cloud platforms (Azure preferred) and modern data warehouses (Databricks, Snowflake, Redshift).
- A proven track record of building resilient data infrastructure in high-performance or financial environments following CI/CD.
- Familiarity with financial data nuances-traditional vs alternative, structured vs unstructured, batch vs real-time-and a sharp eye for point-in-time modelling.
- A detail-oriented mindset, ownership mentality, and strong communication skills in fast-paced, collaborative settings.
