Data Engineer

Verlo is the AI Agent OS for advisors. The product is only as good as the data flowing into it. We're looking for a data engineer to own that data layer end to end. You'll build and own the integration framework that connects Verlo to the systems advisors live in: custodians, portfolio accounting platforms, email, calendars, and more. If you like owning a hard problem from the first byte to the final table, this is for you.

What you'll do

You'll design and build the framework we use to integrate with every external system. That means extracting data from custodians and financial data providers, ingesting email and calendar data, normalizing it into clean models, and loading it into the systems that power our agents. You own the full ETL path: connectors, scheduling, transformation, schema design, data quality, and monitoring.

This is an ownership role. You'll set the patterns for how we add new integrations, decide how we handle messy and inconsistent source data, and build the tooling that lets the rest of the team plug in new sources fast. You'll be on the hook for reliability, accuracy, and the security of financial data as it moves through our pipelines.

What we're looking for

• Strong production experience building data pipelines and ETL or ELT systems
• A track record of integrating with third-party APIs and data sources, including the messy and poorly documented ones
• Solid SQL and data modeling skills, with a sense for designing schemas that hold up as requirements change
• Comfort owning a system end to end: design, build, deploy, monitor, and fix it when it breaks
• Care about data quality, observability, and the handling of sensitive data
• Comfort working with ambiguity in a small, fast-moving team

Nice to have

• Experience with custodial, brokerage, or portfolio accounting data (Fidelity, Schwab, Pershing, Orion, Black Diamond, or similar)
• Experience integrating email and calendar systems (Gmail, Microsoft Graph)
• Familiarity with AWS and modern pipeline tooling (dbt, Airflow, Dagster, or similar)
• Experience preparing data for LLM and agentic workflows
• Prior startup or high-growth environment experience