Senior Backend Engineer: Financial Data
Rogo · New York, NY
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Jul 14, 2026
New York, NY
Jul 21, 2026
What this job asks for AI summary
A senior backend engineering role focused on building and maintaining the data infrastructure that powers an AI platform for financial professionals. Day-to-day work centers on designing large-scale data pipelines, integrating diverse financial data sources, and ensuring data is cleanly structured and reliably served to AI and retrieval systems. Suited to engineers with deep experience in distributed systems, cloud infrastructure, and data-intensive backends who also have a strong interest in financial data.
Senior level · 6+ years · National · Bachelor's required · Full-time
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $138,970 (middle half $107,524–$175,762).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (7)
No work location or city is specified in the posting; the CBSA is left blank. The role appears to be US-based given the company context, but remote status is not explicitly stated — remote=false is a conservative default.
The JD requires a Bachelor's in CS or related field with no 'or equivalent experience' escape clause, so the degree requirement is set to Bachelors.
The strongly-typed language requirement lists Rust, C++, or Java as interchangeable options; Java is additionally called out as 'a strong plus,' so it is also listed separately as a preferred skill.
GCP is listed with a 4-year minimum and 'or equivalent cloud stack' — AWS and Azure are captured as alternatives.
BigQuery, Snowflake, and Redshift are presented as interchangeable columnar store examples; BigQuery is listed first and is the most natural fit given the GCP-primary stack.
The role sits at the boundary between backend software engineering (15-1252) and data/pipeline architecture (15-1243); the primary day-to-day emphasis on building scalable backend services and APIs tips it to 15-1252, with 15-1243 as the runner-up.
Skills such as 'asynchronous patterns', 'queuing', 'caching strategies', 'observability', and 'NoSQL' appear in the requirements section but name no specific tool and are therefore omitted per extraction rules; 'distributed systems' and 'streaming pipelines' are retained as the closest named capability anchors.
Read the full posting
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