Data Engineer

We’re looking for an experienced quant focused data engineer to help us build our data lake. If you’re a curious, creative problem solver with 6–10 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. If you thrive in high-performance environment and love transforming raw data into highly available data products, we would love to hear from you.

Responsibilities:

• End-to-End Pipelines Development: Design, build, and optimize scalable data pipelines for structured and unstructured datasets.
• Core Data Delivery: Collaborate with PMs to deliver timely, and reliable data through user-focused API’s for decision making.
• Quant-Ready Infrastructure: Partner with systematic teams to shape data for advanced analytics and modelling.
• AI-Ready Data: work closely with the AI research, core engineering and UI teams to deliver data suitable for multiple AI use cases.
• 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, with experience in 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— with strong awareness of point-in-time modelling requirements.
• A detail-oriented mindset, ownership mentality, and strong communication skills in fast-paced, collaborative settings.