Software Engineer, Data Platform

Numeric•New York, NY

2d•$125,000 - $250,000•Onsite

About The Position

Numeric is building a modern platform for accounting and finance, addressing the growing data challenges faced by accountants. The company aims to provide live, rich, and trustworthy data and insights for decision-making and execution. Numeric tackles foundational challenges in automating data ingest, transformation, matching, projection, and reporting for a comprehensive financial data graph, integrating data, automation, workflows, and AI-first experiences. The company has achieved product-market fit and is backed by prominent investors and industry leaders.

Requirements

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.
Experience with a modern transformation / orchestration stack (SQLMesh, dbt, Dagster, Airflow, or similar).
Strong general-purpose programming (we’re primarily TypeScript, with Python mixed in).

Nice To Haves

ClickHouse, Iceberg, or Athena experience.
Multi-tenant data isolation, PII handling, or SOC 2 / audit-driven controls.
Prior exposure to financial or accounting data (ERPs, GL, subledgers).
Experience with orchestration systems like Temporal or Inngest

Responsibilities

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.