Staff Analytics Engineer

About the team & 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 (who own infrastructure & ingestion), Data Scientists and business users (who consume your models), and leadership (who rely on your judgment to prioritize), you won't just build models. You'll define how the whole company models, tests, and serves trusted data, and raise the bar for everyone doing it.

Our team is 100% distributed and remote.

Responsibilities:

• Set the multi-quarter technical direction for the warehouse. This covers layering (staging, intermediate, marts), domain boundaries, materialization and incremental strategies, and the 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.

Must-Haves:

• 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 Haves:

• 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.