From the listing
About the role
The Analytics Engineering team builds the warehouse that Alpaca runs on. It turns hundreds of millions of daily events from transactional databases, API logs, CRMs, payment systems, and marketing platforms into the datasets behind partner invoicing, regulatory reporting, executive KPIs, partner-facing products, and a growing number of AI agents.
As a Staff Analytics Engineer, you'll own the future of our warehouse: how it's modeled, how it performs, and how it's served. Working across Data Platform, Data Scientists, business users, and leadership, you'll define how the whole company models, tests, and serves trusted data, and raise the bar for everyone doing it.
What you'll do
- Set the multi-quarter technical direction for the warehouse, covering layering (staging, intermediate, marts), domain boundaries, materialization and incremental strategies, and standards that keep more than 1,000 dbt models fast, testable, and cost-efficient as we grow 10x.
- Decide which models are served through ad hoc OLAP queries on the lakehouse, which belong in a real-time analytics engine for low-latency internal and partner-facing use, and which become governed metrics in a semantic layer.
- Agree on schemas, freshness SLAs, and change notification with source owners, and choose CDC or batch for each source based on what consumers need.
- Guarantee cent-level accuracy on financial data with tests, reconciliation, and write-audit-publish patterns that keep silent errors from shipping.
- Set org-wide standards for modeling, testing, and CI/CD, mentor engineers, and win alignment across teams. Your impact should show up in others' work.
- Prioritize work that moves metrics for finance, operations, and partners, and head off scaling, quality, and cost problems before they hit.
What they're looking for
- 7+ years in analytics engineering or data engineering focused on transformation and warehouse architecture, with Staff-level leadership: setting direction and leveling up teams.
- Has led an org-level warehouse migration or redesign, such as a mart restructure, semantic layer rollout, or testing and CI overhaul.
- Deep query-performance expertise on distributed engines such as Trino, Presto, or Spark. This includes reading query plans, partitioning and file layout on Apache Iceberg, and incremental materialization.
- A clear point of view on when a dataset belongs in a lakehouse query engine, a real-time OLAP engine such as ClickHouse, or a semantic layer.
- Expert-level SQL and dbt, plus Python for transformations beyond SQL.
- Experience defining data contracts and freshness SLAs with upstream producers, and an understanding of the trade-offs between CDC and batch ingestion.
- Thrives in ambiguity. Can take a vague, high-stakes problem from 0 to 1 with minimal oversight.
Nice to have
- Production experience modeling data for ClickHouse or another real-time OLAP engine.
- AI and agentic analytics: making data usable by LLMs and agents, building evals.
- Familiarity with Airflow, Airbyte, and CDC tooling such as Debezium or Redpanda.
- Brokerage or financial-markets domain experience.
Benefits, as listed
The hot takeJeff's read on Alpaca
I like their clear views on the value of remote work, check out this quote from their careers page:
> "Our teams are not just global, we're also 100% remote and have team members in almost every time zone and continent (except Antarctica). By joining Alpaca you'll be part of a multicultural, international organization."
I also like that their Glassdoor scores are going up (now 3.7/5), and they have some solid testimonials. I'm a LITTLE wary of this statement that gives me a bit of a "hustle culture" vibe:
> "We're keen on that last 10% of effort and believe that the details do matter. If this sounds like you, then you're in the right place."