Staff Analytics Engineer

Who We Are:

Alpaca is a US-headquartered, global leader in agent-first brokerage infrastructure for stocks, ETFs, options, crypto, fixed income, 24/5 trading, and more. Amongst our subsidiaries, Alpaca is a licensed financial services company, serving hundreds of financial institutions across 40 countries with our institutional-grade APIs. This includes broker-dealers, investment advisors, wealth managers, hedge funds, and crypto exchanges, totalling over 10 million brokerage accounts. Our global team is a diverse group of experienced engineers, traders, and brokerage professionals who are working to achieve our mission of opening financial services to everyone on the planet. We're deeply committed to open-source contributions and fostering a vibrant community, continuously enhancing our award-winning, developer-friendly API and the robust infrastructure behind it. Alpaca is proudly backed by $400 million in funding from top-tier global investors including Portage Ventures, Spark Capital, Tribe Capital, Social Leverage, Horizons Ventures, Opera Tech Ventures, SBI Group, Derayah Financial, Unbound, Peak XV, Elefund, and Y Combinator. Our Team Members:

We're a dynamic team of 400+ globally distributed members who thrive working from our favorite places around the world, with teammates spanning the USA, Canada, Japan, Hungary, Nigeria, Brazil, the UK, and beyond! We're searching for passionate individuals eager to contribute to Alpaca's rapid growth. If you align with our core values—Stay Curious, Have Empathy, and Be Accountable—and are ready to make a significant impact, we encourage you to apply.

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.

How We Take Care of You: