Farseer is a fast-growing startup building an enterprise software stack from first principles. We aim to become the underlying database and operating system for every business-critical app, from financial planning and analytics to accounting and beyond. Farseer empowers humans to build enterprise software with the help of AI.

Over the next decade, we will rebuild the enterprise nervous system. We are starting with finance, a real-time collaborative planning and reporting platform that turns finance teams into proactive strategists. Once that foundation is solid, the same AI-native core will extend across the operational stack, reporting, supply chain management, manufacturing, procurement, revenue ops, and more.

Our mission is to build enterprise software that people enjoy using. No user-hostile screens. No bloated workflows. Just fast, intuitive tools that help teams solve real business problems.

What you'll do

We're building our own data platform — the infrastructure that ingests, transforms, validates, and serves enterprise financial data inside Farseer. As a Data Engineer, you'll focus on two things::

Build data pipelines for real customers. You'll extract data from ERPs, databases, APIs, and spreadsheets into Farseer's calculation engine. You'll transform and validate that data, deliver UAT reports proving the numbers match the source system, and work directly with client finance and IT teams to understand their data. Every implementation you do makes the next one faster — because you're also building the platform.

Build the data platform itself. You'll help design and build the system that makes all of this scalable: declarative pipeline definitions, schema validation, a reusable connector library, orchestration, freshness monitoring, alerting, and eventually a customer-facing dataset health UI. This is greenfield work — we're not maintaining someone else's platform, we're creating our own.

You'll join a small, but growing team and have an outsized impact on how the platform takes shape.

What we're looking for

You don't need to check every box — if you're strong in some of these and eager to learn the rest, we'd love to hear from you.

You might be a good fit if you:

• Can write production-quality Python

• Have meaningful experience building data pipelines or data platform infrastructure — extraction, transformation, validation, loading

• Are comfortable working directly with clients — understanding their systems, discussing data requirements, and presenting results

• Understand the data platform problem space — orchestration (Dagster, Prefect, Airflow), extraction frameworks (DLT, Airbyte, Fivetran), data validation (Great Expectations, Soda), columnar formats (Parquet, DuckDB, Arrow). You've used some of these tools, or built similar things yourself.

• Can navigate cloud infrastructure (GCP, AWS) and know how to run systems in production — monitoring, alerting, debugging when things break

• Are comfortable working with AI tools to move faster — LLMs for code generation, debugging, or prototyping

Nice to have:

• Experience with financial data (GL, P&L, budgeting, forecasting, ERP integrations)

• Full-stack development experience — the role includes building customer-facing UI for dataset health and lineage

• Startup or fast-paced product engineering environments

• You read job descriptions thoroughly. Mention "Brent Spiner" somewhere in your application, and we'll know.

A note about the role

This is not a role where you inherit a well-oiled machine and keep it running. You'll be building things that don't exist yet, figuring out the right approach as you go, and iterating fast based on what works. If that energizes you — we should talk.

If that sounds exciting — hit apply.