Software Engineer
US$220,000 – US$262,500 / yr base salary
Posted 11 Sep 2026
Advertised as “Staff Software Engineer - Analytics & Data Architecture”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- Remote
- Contract
- Full-time
| Base salary | US$220,000 – US$262,500 / yr |
|---|
Shape the future of trust in the age of AI
At Oscilar, we're building the most advanced AI Risk Decisioning™ Platform. Banks, fintechs, and digitally native organizations rely on us to manage their fraud, credit, and compliance risk with the power of AI. If you're passionate about solving complex problems and making the internet safer for everyone, this is your place.
Why join us:
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Mission-driven teams: Work alongside industry veterans from Meta, Uber, Citi, and Confluent, all united by a shared goal to make the digital world safer.
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Ownership and impact: We believe in extreme ownership. You'll be empowered to take responsibility, move fast, and make decisions that drive our mission forward.
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Innovate at the cutting edge: Your work will shape how modern finance detects fraud and manages risk.
About the role
You'll own the data layer behind our analytics product and design of our analytics architecture and data model. Today that's a high-performance analytical store serving customer-facing queries; where we're headed is a tiered platform that separates our system of record, a low-latency serving tier, and a batch/ML tier. You'll keep the serving layer fast and reliable while designing and building that broader architecture, deciding where each workload lives based on measured latency, cost, and isolation.
This is a hands-on engineering role with strong architectural influence. You'll write code and also shape the multi-quarter direction of the analytics platform.
What you'll do
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Find and fix database performance and scaling problems.
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Own the schema and data model for our analytical data.
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Design and build our analytics architecture: a tiered platform spanning our system of record, a low-latency serving tier, and a batch/ML tier.
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Decide where each workload runs across those tiers, backing placement decisions with real benchmarks rather than vendor claims.
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Build observability into the data layer so problems surface early.
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Partner with the ingestion team to keep the read and write paths coherent as schemas evolve.