Technical Lead, Data & Inference

At Primer, we're building the intelligence layer for modern go-to-market teams. Our platform lets customers join messy datasets, connect siloed marketing + sales tools, and run automated acquisition playbooks—powered by scalable APIs, ETLs + Reverse ETLs, distributed systems, and DAG orchestration (React/Node + Python, plus tools like Prefect and dbt). We process terabytes daily, aim for sub-second query responses, and optimize batch processing to magically behave like real-time.

Our mighty, small team ofachieved 3.8× growth in 2025 with everyone making an outsized impact.

We’re refreshingly novel in the crowded go-to-market landscape: our work is patent‑pending across real-time data, graphics, inference, and AI recommendations—so this isn’t “maintain the dashboards” energy. And we run with trust: engineers spend >90% of their time outside meetings, aiming for outcomes over theatrics.

Role Description

As Technical Lead, Data & Inference, you’ll lead the design, delivery, and scaling of our end‑to‑end data platform—from ingestion to insights—so the rest of the company can move fast without guessing. You’ll turn diverse third‑party APIs and heterogeneous sources into trusted, low‑latency pipelines, uphold data quality + SLAs, and raise the standard through mentorship and hands‑on technical leadership.

What you’ll do:

• Build and ship batch + streaming pipelines; prototype, profile, and harden the critical paths. Mentor, review, and lift the bar.

• Own reliability, cost, and SLOs: observability + capacity planning, ≥99.9% uptime, minutes‑level latency, and cost/TB efficiency—plus RCAs that end in fixes that stick.

• Run pragmatic inference pipelines that augment data flows (enrichment, scoring, QA with LLMs/RAG) with versioning, canaries, caching, and eval loops.

• Partner cross‑functionally to build “data as a product” a la Netflix: contracts, ownership, lifecycle management, and usage‑based impact.

• Drive pipeline + data lake architecture: crisp design reviews, documented trade‑offs/lineage/reversibility, and clear build‑vs‑buy calls others can trust.

This is in‑person in the SF Bay Area, with most days near Embarcadero. We like collaborating in person, we’re looking for A‑players, and we’re willing to pay for it.

Qualifications

• 6+ years building and scaling production‑grade data systems, with leadership experience on data engineering/platform teams (architecture, modeling, pipeline design).

• Expert SQL (optimization on large datasets) + strong Python; hands‑on with distributed data tech (e.g., Spark/Flink/Kafka) and orchestration (Airflow/Dagster/Prefect).

• Experience scaling integrations with third‑party APIs/internal services; strong instincts around concurrency/parallelization and classic processing patterns.

• Familiarity with dbt, columnar databases (DuckDB, Click House), and modern data stack fundamentals;
IaC/CI/CD/observability basics (Kubernetes exposure is a plus).

• Strong fundamentals (BS/MS in CS/CE/EE or similar) and strong async communication across distributed teams.

• Bonus: very good Node.js—it helps you ramp faster in our stack.

More details can be found at

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