Analytics Engineer

Alpaca is a US-headquartered global leader in agent-first brokerage infrastructure for stocks, ETFs, options, crypto, fixed income, 24/5 trading, and more. Through its subsidiaries, Alpaca is a licensed financial services company serving hundreds of financial institutions across 40 countries with institutional-grade APIs. Its customers include broker-dealers, investment advisors, wealth managers, hedge funds, and crypto exchanges, with more than 10 million brokerage accounts. Alpaca is backed by $400 million in funding. The Analytics Engineering team builds the warehouse that turns events from transactional databases, API logs, CRMs, payment systems, and marketing platforms into datasets for partner invoicing, regulatory reporting, executive KPIs, partner-facing products, and AI agents.

• Set the multi-quarter technical direction for the warehouse, including layering (staging, intermediate, marts), domain boundaries, materialization and incremental strategies, and standards to keep more than 1,000 dbt models fast, testable, and cost-efficient as the company grows 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 notifications with source owners; choose CDC or batch for each source based on consumer needs.
• Guarantee cent-level accuracy on financial data with tests, reconciliation, and write-audit-publish patterns that prevent silent errors from shipping.
• Set organization-wide standards for modeling, testing, and CI/CD; mentor engineers and build alignment across teams.
• Prioritize work that moves metrics for finance, operations, and partners, and address scaling, quality, and cost problems proactively.

• 7+ years in analytics engineering or data engineering focused on transformation and warehouse architecture, with Staff-level leadership setting direction and developing teams.
• Led an organization-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, including reading query plans, partitioning and file layout on Apache Iceberg, and incremental materialization.
• Clear judgment about 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 understanding of the trade-offs between CDC and batch ingestion.
• Able to work through ambiguity and 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 experience, making data usable by LLMs and agents and building evals; familiarity with Airflow, Airbyte, and CDC tooling such as Debezium or Redpanda; brokerage or financial-markets domain experience.

• The team is 100% distributed and remote.
• Competitive salary and stock options; health benefits.
• One-time USD $500 new-hire home-office setup and a USD $150 monthly stipend via a Brex Card.