Staff Engineer – Data & Inference

Your Mission

Lead the design, delivery, and scaling of Primer's end-to-end data platform—owning the full lifecycle from ingestion to insights—to make data reliable, fast, and useful across the company. You'll turn diverse, third-party APIs and heterogeneous sources into trusted, low-latency pipelines; uphold data quality and SLAs; and amplify impact by spreading best practices and data-driven thinking across teams.

What you'll do

Be a force multiplier: design and ship scalable batch + streaming pipelines; prototype, profile, and harden critical paths.

Own reliability, cost, and SLOs: instrument observability and capacity; drive ≥99.9% uptime, minutes-level latency, and cost/TB efficiency; lead RCAs and land durable fixes.

Operate pragmatic inference pipelines that augment data flows (enrichment, scoring, QA with LLMs/RAG); manage versioning, canarying, caching, and evaluation loops.

Partner cross-functionally to build "data as a product" a la Netflix: define audience and contracts, ensure reliability and ownership, manage lifecycle (versioning→sunsetting), and validate impact through usage.

Drive pipeline and data lake architecture: run design reviews, document trade-offs/lineage/reversibility, and make clear build-vs-buy calls others can safely build on.

Scale integrations with third-party APIs and internal services; resolve data conflicts, ensure quality, and support both sub-second query paths and large batch workflows.

You'll succeed by having

Deep empathy for data consumers (analysts, product, business stakeholders) and a pragmatic approach that balances speed, cost, and accuracy for business impact.

Proactive, honest communication; comfort working hybrid in-person and with a distributed team; ability to set high standards and operate beyond your comfort zone.

A default-to-teach ethos—mentoring others, documenting schemas/lineage/trade-offs—and the judgment to choose build vs. buy wisely; acts as a clear, calm proxy for data/infra decisions.