Role Overview
We are looking for a creative and enthusiastic Data Engineer to join our team. In this role, you will help build and maintain scalable data platforms and pipelines that power analytics, applications, and decision-making across the organization. You will work with a wide range of structured and unstructured datasets, design reliable data models, and develop services that make data accessible and useful to end users.
The ideal candidate has experience building and supporting modern data infrastructure, working with large-scale datasets, and creating high-quality, analytics-ready data products. This role requires strong technical skills, attention to detail, and the ability to collaborate closely with both technical and business stakeholders.
In this role, you will:
• Develop cloud-first data ingestion processes using Python, SQL, and Spark
• Engineer data models and infrastructure for a wide variety of market and alternative datasets
• Design and build services and plugins to enhance our Data Acquisition Platform
• Maintain alerting systems to ensure smooth day-to-day operations for hundreds of datasets
• Author tests to validate data quality and the stability of the platform
• Build and support AI-enabled workflows that accelerate dataset onboarding, pipeline creation, metadata generation, and natural-language access to data
• Design and evolve rules-based data quality frameworks that automatically validate completeness, freshness, schema integrity, and business logic across datasets
• Investigate and defuse time-sensitive data incidents
• Communicate with data providers to onboard new datasets and troubleshoot technical issues
• Evangelize best practices to partners throughout the firm
• Work directly with Analysts, Quants, and Portfolio Managers to understand requirements and provide end-to-end data solutions
What You'll Bring
• Bachelor's or Master's degree in Computer Science or a related field
• Strong analytical, data, and programming skills (Python, SQL, NoSQL)
• Strong experience with Snowflake and building analytics-ready datasets in a modern cloud data warehouse
• 3+ years of experience with at least one of Spark, Hive, or Hadoop
• 2+ years of experience orchestrating pipelines with technologies such as Airflow, Luigi, Oozie, or NiFi
• 1+ years of experience with cloud technologies (AWS, Azure, or Google Cloud)
• Solid understanding of time series data and temporal queries
• Experience with large datasets and techniques to architect them for performance
• Ability to understand and contribute to existing data systems software
• Strong oral and written communication skills; must be a team player
Nice to Have
• Experience with Go
• Aptitude for designing infrastructure, data products, and tools for Data Scientists
• Financial industry experience
