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Rogo

Software Engineer: Financial Data

New York City$250k–$315kfulltimemidAdded 2 days ago

About this role

Join Rogo's Financial Data team as a Backend Engineer to build and operate the critical infrastructure that powers AI-driven financial analysis for elite investment firms. You'll architect scalable data pipelines that ingest, transform, and serve structured and unstructured financial datasets while working closely with AI teams to ensure data quality and performance at scale.

What you'll do

  • Design and build scalable data processing pipelines that ingest millions of financial documents and datapoints daily with low latency
  • Integrate heterogeneous financial data sources—market vendors, proprietary platforms, third-party providers—into reliable, queryable systems
  • Develop secure, compliant data integrations that meet enterprise standards for privacy, reliability, and trust
  • Partner with Core AI team to structure and serve financial data that maximizes model performance and retrieval accuracy
  • Own end-to-end data infrastructure from conception through production deployment and operations
  • Lead by example through high-quality code and decision-making that elevates team standards

What they're looking for

  • Python (backend and APIs)
  • Strongly typed languages (Java preferred, Rust, or C++)
  • GCP or equivalent cloud platforms
  • Distributed systems design (async patterns, streaming, queuing, caching)
  • Relational databases, SQL optimization, and schema design
  • NoSQL, in-memory (Redis), and columnar databases (BigQuery, Snowflake, Redshift)
  • Large-scale data pipeline architecture (streaming and batch)
  • Observability (logging, metrics, tracing)
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Rogo

Rogo builds a financial AI platform with a focus on data-driven insights and user experience. The company is hiring Analytics Engineers to develop data pipelines and reporting tools, and Design Engineers to enhance its design system and user interactions.

View all jobs at Rogo

Likely interview questions

  • Walk us through your experience designing and operating large-scale data pipelines—how did you ensure reliability and throughput?
  • Describe a complex data integration project involving heterogeneous sources. How did you handle data quality and transformation challenges?