The Role

As the driving force behind Schonfeld’s next-generation data platforms, you will architect, operate, and continually refine underlying platform that empowers research, systematic trading, AI and risk analytics. On top of this robust foundation, you’ll build and run high-throughput pipelines spanning ultra-low-latency market-data streams to cost-efficient end-of-day workflows. Partner closely with quants, data scientists, and fellow engineers to achieve sub-minute SLAs, shape firm-wide architecture, and mentor peers. We believe diverse backgrounds and voices spark better ideas and more resilient systems, so you’ll join an inclusive culture where every perspective counts.

What you’ll do

• Architect and operate the platform – 24x7 reliability, IaC-driven deployments, tight cost controls
• Build and evolve batch and streaming pipelines that ingest billions of market-data events each day
• Architect high-performance, cost-efficient Lakehouse tables
• Expose curated datasets via robust, versioned APIs consumed by quants and AI agents
• Optimize workflows to meet aggressive SLAs, embedding observability and automated data quality checks end-to-end
• Champion Infrastructure-as-Code with Git-driven CI/CD for every data asset
• Mentor junior engineers and enforce best-in-class coding standards
• Continuously refine processes to keep our data ecosystem resilient and lightning-fast

What you’ll bring
What you need:

• 7+ years building production data platforms with Python and SQL
• Deep expertise in big-data architecture: partitioning, sharding, columnar formats
• Hands-on with SingleStore/MemSQL, Spark/Flink/EMR and event buses (Kafka/Kinesis)
• Proven AWS skills plus Terraform / CloudFormation infrastructure-as-code mastery
• Experience delivering data services for AI/ML workloads and feature pipelines
• Performance-tuning Postgres, SingleStore/MemSQL, or KDB for sub-second queries
• Proficient in API design, versioning, and OpenAPI/Swagger documentation
• Comfortable collaborating directly with traders, quants, and data scientists

We’d love if you had:

• Market-data domain expertise (tick, options, macro)
• Mastery of Iceberg, Delta Lake, or Hudi on S3
• Hands-on work with real-time analytics engines such as SingleStore/MemSQL
• Experience building AI/ML feature stores or RAG pipelines
• Skill with performance profiling tools
• Contributions to open-source data tooling or technical talks

Who we are  
Schonfeld is a global multi-manager hedge fund that strives to deliver industry-leading risk-adjusted returns for our investors. We leverage both internal and external portfolio manager teams around the world, seeking to capitalize on inefficiencies and opportunities within the markets. We draw from decades of experience and a significant investment in proprietary technology, infrastructure and risk analytics to invest across four main strategies: Quant, Tactical, Fundamental Equity and Discretionary Macro & Fixed Income.

Our Culture
At Schonfeld, we’ll invest in you. Attracting and retaining top talent is at the heart of what we do, because we believe that exceptional outcomes begin with exceptional people. We foster a culture where talent is empowered to continually learn, innovate and pursue ambitious goals. We are teamwork-oriented, collaborative and encourage ideas—at all levels—to be shared. As an organization committed to investing in our people, we provide learning and educational offerings and opportunities to make an impact. We encourage community through internal networks, external partnerships and service initiatives that promote inclusion and purpose beyond the firm’s walls.

The base pay for this role is expected to be between $150,000 and $190,000. The expected base pay range is based on information at the time this post was generated. This role may also be eligible for other forms of compensation such as a performance bonus and a competitive benefits package. Actual compensation for the successful candidate will be determined based on a variety of factors such as skills, qualifications, and experience.


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