> Markdown version of [/jobs/ext/2916049-software-engineer-data-platform](https://www.wearedevelopers.com/jobs/ext/2916049-software-engineer-data-platform). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer, Data Platform - **Company:** Ai, Inc - **Location:** New York, NY, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Query Performance, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Batch Processing, Data Infrastructure, Extract Transform Load (ETL), Data Security, JSON, Python (Programming Language), PostgreSQL, Netsuite, Node.Js, Raw Data, Standard Sql, Software Engineering, SQL Databases, TypeScript, Stripe, Data Lakehouse, Vertica, Data Pipelines - **Published:** September 15, 2026 - **Apply:** https://startup.jobs/software-engineer-data-platform-numeric-10064950 ## About the Role * 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 have * 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 Stack: TypeScript / Node, Postgres, ClickHouse (ClickPipes), SQLMesh, S3 / Athena, Inngest, AWS. ## Description 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., * 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. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json) - [Navigating Growth, Scaling Challenges, and Office Expansions with David Singleton, CTO at Stripe](https://www.wearedevelopers.com/videos/100362-navigating-growth-scaling-challenges-and-office-expansions-with-david-singleton-cto-at-stripe) - [Analytics in the Age of Agentic AI: A tour of ClickHouse and Langfuse](https://www.wearedevelopers.com/videos/100240-analytics-in-the-age-of-agentic-ai-a-tour-of-clickhouse-and-langfuse) - [Introducing JSON Structure](https://www.wearedevelopers.com/videos/100219-introducing-json-structure) - [Build Your Own Subscription-based Course Platform](https://www.wearedevelopers.com/videos/321-build-your-own-subscription-based-course-platform) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again)