Rogo
Software Engineer: Financial Data
- Confirmed live in the last 24 hours
- $250k–$315k
- Mid level
- Full-time
- On-site · New York City
- 5+ yrs exp
- Added 1 month ago
About this role
Join Rogo's Financial Data team as a Backend Engineer to build and scale critical data infrastructure powering AI-driven financial analysis. You'll design robust pipelines ingesting and transforming vast financial datasets from multiple sources, ensuring the data quality that investment professionals demand.
What you'll do
- Design and build scalable data processing pipelines handling millions of documents daily with low latency
- Architect systems integrating heterogeneous financial data sources into reliable, queryable platforms
- Own end-to-end data infrastructure from conception through production deployment
- Collaborate with Core AI team to optimize data structure and quality for model performance
- Build secure, compliant data integrations meeting enterprise privacy and reliability standards
- Lead by example through code quality and decision-making that elevates the team
What they're looking for
- Python backend and API development
- Strongly-typed languages (Java preferred, Rust, or C++)
- Google Cloud Platform (GCP) or equivalent cloud infrastructure
- Distributed systems design (async patterns, streaming, queuing, caching)
- SQL and relational database optimization
- NoSQL, Redis, and columnar data stores (BigQuery, Snowflake, Redshift)
- Large-scale data pipeline architecture (streaming and batch)
- Observability systems (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.
- Industry
- Technology & Software
Likely interview questions
- Walk us through a complex data pipeline you've built at scale—what were the key architectural decisions and how did you handle failures?
- Describe your experience integrating data from multiple heterogeneous sources. What challenges did you face and how did you ensure reliability?