Your Role:

We are looking for a Head of Data to lead Alpaca’s ~15-person data department across Platform Engineering, Analytics Engineering, and Data Science. You will own the data strategy, the team’s execution, and the department’s roadmap.

The Data department serves every function at Alpaca: it powers partner invoicing and revenue attribution, provides the analytical foundation for sales, product, and compliance, and operates the data platform that processes hundreds of millions of events daily. You will manage three team leads, balancing operational delivery (invoicing, embedded analytics, regulatory reporting) with strategic bets (self-service warehouse, AI-powered analytics, enterprise search).

This is a player-coach role. You will set direction for the team while staying close enough to the technical details to make architecture decisions, unblock your leads, and represent data’s capabilities to the executive team.

Things You Get To Do:

• Lead and develop three sub-teams: Platform Engineering & ETL, Analytics Engineering, and Data Science & Analytics. Manage leads, set priorities, and ensure delivery.
• Own the Data Lakehouse architecture: Trino, Iceberg/GCS, Airflow, Airbyte, Redpanda CDC, dbt. Make build-vs-buy decisions on tooling.
• Drive partner invoicing accuracy and evolution: ensure invoicing logic is versioned, reproducible, and scales with new pricing mechanisms and product launches.
• Deliver embedded analytics: expose warehouse data to partners via BrokerDash, SSR pipelines, and API-based reporting. Own row-level security and entitlements.
• Support product launches with data change management: coordinate data impact analysis for new products (fixed income, global stocks, perps, 24/5 trading) across downstream datasets, dashboards, and reverse ETL.
• Accelerate self-service: move the organization toward self-serve analytics via semantic layers, data catalogues, and conversational BI so the data team can shift from ad-hoc queries to strategic projects.
• Guide AI/ML enablement: oversee enterprise AI search, agent-based workflow automation, and LLM-powered analytics. Help balance vendor solutions with in-house development.
• Collaborate with Finance, Sales, Product, Compliance, and Customer Success to translate business needs into data products.
• Manage infrastructure costs: keep data + cloud cost ratio under target as AUC grows.
• Operate production systems: own on-call processes, incident response, and SLOs for data freshness, accuracy, and availability.

Who You Are (Must-Haves):

• 8+ years in data engineering or analytics, including 3+ years managing data teams (leads + ICs).
• Deep experience with modern data stack: dbt, Trino/Presto or equivalent query engines, Apache Iceberg or similar table formats, cloud object storage.
• Hands-on experience with ETL/ELT patterns at scale: CDC (Debezium/Kafka), batch (Airflow/dbt), streaming, and reverse ETL.
• Track record of building self-service analytics capabilities for non-technical stakeholders.
• Experience with financial data: trading, invoicing, revenue attribution, or regulatory reporting in fintech or financial services.
• Proficiency in Python and SQL. Comfortable reading code, reviewing PRs, and making architecture decisions.
• Experience managing distributed/remote teams across multiple time zones.
• Strong stakeholder management: you can translate between executive priorities and engineering execution.
• Experience with GCP (GKE, GCS, BigQuery migration), Kubernetes, Helm, Terraform.

Who You Might Be (Nice-to-Haves):

• Experience with brokerage or broker-dealer operations (clearing, settlement, market making, reconciliation).
• Familiarity with LLM/AI tooling: MCP, vector databases, enterprise search, conversational BI (WrenAI, Cube).
• Background in compliance analytics (AML/Actimize, KYC, margin calls).
• Exposure to open-source data catalogues (OpenMetadata, Collate) and data quality frameworks.