Software Engineer, Data Platform

Why Numeric

Every business runs on accounting, but the systems underneath it were built for a different era. Today’s ERPs don’t store a company’s financial data. They store a diluted copy of it. Every contract, lease, and invoice gets flattened into a journal entry, and the context that made it meaningful is thrown away. That’s why accounting teams spend every month re-checking numbers, stitching together spreadsheets, and waiting on engineering, and why AI struggles to do real accounting work. Accounting doesn’t have a reporting problem. It has a data problem.

Enter Numeric: the data platform finance teams run on. We started with the month-end close, where the data problem hurts most, and hundreds of finance teams now run their close on Numeric. That work led us to rebuild the system of record itself. In September 2026 we launched the Financial Data Platform (FDP) , a platform built for the agent era. Instead of a journal entry, the real business object enters the system and the accounting is derived from it. Every number is traceable to its source, reporting works on any dimension, and accountants and AI agents finally have enough context to work together. While others bolted AI features onto the old data model, we built a new one.

We have a growing base of customers who love our product, including Anthropic, Plaid, Brex, Betterment, Wealthfront, and Asana. We’ve raised $89M from top-tier investors including IVP, Menlo Ventures, Founders Fund, and 8VC, along with founders and executives from Ramp, Segment, and other category-defining companies. We’re hybrid but in-office first, with teams in New York, San Francisco, and London, and we just shipped the product that will define our next decade. There’s never been a better time to join.

About the role

Numeric’s product is, at bottom, data: your accounting history, your ability to pass an audit, your capacity to catch mistakes before your auditors do. The Data Infrastructure team builds the financial data plane that makes that possible — the lakehouse that ingests raw data from ERPs, banks, payment processors, and customer warehouses, transforms it into typed canonical tables, and serves it to our accounting modules, reporting engine, and AI agents. We’re mid-way through a deliberate architectural bet (ClickHouse-backed lakehouse, SQLMesh, per-tenant isolation) and it is the layer we expect to carry the most new investment this year. Every engineer on the team owns a slice of the architecture end to end.

What you might work on

Ingestion at scale: incremental, watermark-based extraction from API sources (NetSuite, Stripe, Brex, Ramp, HubSpot…), file-based S3 inboxes, and Postgres → ClickHouse replication — and making each new source cheap to add.

Data contracts & typed source data: stop downstream consumers from touching untyped raw JSON; enforce schemas at the boundary and make datasets discoverable.

Data Lakehouse schema management: a single declare → codegen → apply pipeline so schema intent is codified, reviewable, and applied consistently across tenants.

Data Security: Scoping queries, building ACLs, and ensuring that we never, ever compromise our customer’s trust

Batch processing & event-driven accounting: decouple event processing from downstream failures; give operators clear “what ran, what failed, and why” answers.

Data correctness & observability: completeness checks rooted in platform data, ingestion health, two-phase re-ingest, and explanatory links from ledger entries back to raw events.

Reporting scalability: the query patterns and materializations that let reporting drill from a board-level P&L to a single transaction without falling over.

Tons of other stuff!

You might be a great fit if you…

Have built or operated data pipelines / lakehouse / warehouse systems in production and have opinions about ETLs, idempotency, watermarks, and backfills.

Treat auditability and “fail early, fail explicitly” as design constraints, not afterthoughts.

Are comfortable across the stack: TypeScript services, SQL (ClickHouse and Postgres), and transformation tooling like SQLMesh or dbt.

Like being close to the consumers of your data — accounting module engineers, the reporting team, and agents — and shaping the interfaces they use.

Want to learn how accountants think about source data, completeness, and close, and let that shape what you build.

What you need

3+ years of software engineering experience, with meaningful time on data-intensive systems.

Strong SQL and a working understanding of columnar stores, partitioning, and query performance.