Data Engineer

Most wealth management is fragmented. Range is building AI-powered wealth management that actually works. One platform – investments, taxes, retirement, estate – all of it. All helping members manage financial complexity. Backed by Google's Gradient Ventures, Cathay Innovation, and Scale Venture Partners. Hyper-growth. Small team. Every person matters.

About the role

As a Senior Data Engineer at Range, you’ll be the technical owner of Range’s data infrastructure at a pivotal moment. We’ve grown organically and have different warehouses and a patchwork of ingestions, processes and standards. We’re building the platform, practices and core data model that the next stage of growth depends on. You’ll define the standards, architecture and foundational data model that will shape how we leverage data for years. You’ll also build governance into the foundation that supports our current regulations and the ones we are growing into.

This role is ideal for a senior data engineer who's done this before with strong opinions on dimensional modeling and data architecture, comfortable making the foundational calls in a fast-moving environment where the data is regulated.

We're excited to hire this role at Range's Headquarters in McLean, VA. All of our positions follow an in-office schedule Monday through Friday, allowing you to collaborate directly with your team. If you're not currently based in the area, but love what you see, let’s discuss relocation as part of your journey to joining us.

What you’ll do with us

• Own the foundational data model. Design how our core entities look and are structured in the warehouse. Make the calls on grain, slowly changing dimensions, snapshot strategy, and the conventions our staging and mart layers are built on.

• Set the standards. Define how we do testing, documentation, orchestration, and alerting across all our data infrastructure and bring our existing warehouses up to that bar.

• Build and operate the ingestion layer. Design and maintain pipelines that move data reliably from internal and external sources into the warehouse, using Fivetran, dbt and Python where each fits best. Implement the data quality checks, alerting, and troubleshooting tools that let us catch issues before stakeholders do.

• Architect for governance. Build role-based access, and audit controls into the foundation so that governance scales with the platform instead of being retrofitted later.

• Help shape the platform decision. We're evaluating our long-term data platform and whether to consolidate on what we have or move to something new. You'll partner with our Head of Data and Analytics on the evaluation and own the migration end-to-end once the decision is made.