Ridgeline · San Ramon, CA

Salary
$138,500–$173,000from the description
Posted
Jul 23, 2026
Location
San Ramon, CA
Last confirmed open
Jul 24, 2026

What this job asks for AI summary

A senior engineering role focused on building and owning the data and AI platform infrastructure for a fintech software company. Day-to-day work involves designing secure API integrations, Model Context Protocols, and AI components such as RAG pipelines and vector databases, while also contributing to broader data architecture strategy. Suited to an experienced engineer comfortable with distributed systems, modern data platforms, and cloud infrastructure who can operate with significant autonomy.

Senior level · 7+ years · San Francisco-Oakland-Hayward, CA · Full-time

Advertised as Staff, but the requirements read as Senior.

Must have (10)
distributed systemsAPI integrationsOAuth 2.0SSOModel Context ProtocolSnowflakevector databases, Pinecone, Weaviate or PgvectorAWSTerraformPython
Nice to have (4)
RAG pipelinesLLMsmodel provider gatewaysClaude Code or Cursor

“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

Roughly 65 people in the San Francisco-Oakland-Hayward, CA area plausibly meet what this posting asks for (software developers). range 25–95

Applicant volume Light — Few people clear these requirements, so an application that does clear them gets looked at. Worth applying to even if you miss a nice-to-have.

What gives you an edge
Model Context Protocol2%vector databases6%Snowflake8%Terraform11%

Rare in this occupation — lead with these, and say what you built with them.

What won't set you apart
Python51%

Most people in this occupation already list these. Still required — just not what gets you shortlisted.

What the occupation pays Median $190,744 (middle half $167,095–$224,501). This posting is about at that midpoint.

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 (8)

The role is titled 'Staff AI Data Platform Engineer' but the actual requirements — 7+ years of experience, senior-level ownership of a workstream, mentoring peers — are consistent with a Senior engineer rather than a true Staff-level role with cross-team technical authority. Seniority is assessed as Senior accordingly.

The posting lists two primary locations: San Ramon, CA and Reno, NV. San Ramon falls within the San Francisco-Oakland-Hayward CBSA (41860); Reno, NV (CBSA 39900) is the secondary location. The San Francisco Bay Area CBSA is used as primary given it is listed first.

MCP (Model Context Protocol) experience is listed as required but the posting explicitly acknowledges the protocol is less than two years old and does not expect years of MCP-specific tenure — what is gated is proven judgment in secure integration architecture that transfers to MCPs.

Terraform is listed with 'or similar' — alternatives such as Pulumi or AWS CloudFormation would be accepted substitutes.

Python is listed with 'or a comparable language' — the posting names Python specifically in the requirements section, so it is treated as the primary named technology.

AI-assisted coding tools (Claude Code, Cursor) appear in the requirements section as evidence of personal AI fluency, but the gate is on demonstrated AI workflow adoption rather than a specific tool — listed as preferred to reflect the posting's framing ('use AI-assisted coding tools like Claude Code or Cursor').

Data lineage, access control, and data quality practices are mentioned as required familiarity but name no specific tool or platform, so they are omitted from the skills list per the no-generic-concepts rule.

RAG pipelines appear in both the required section (as part of the AI platform the team builds) and the Nice to Have section (production experience). The required/preferred flag flag reflects the Nice to Have placement for production-level proficiency; the team's use of RAG is context, not a candidate gate.

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