Data Engineer

Description

The company is building an AI system that runs order-to-cash work for large enterprises, covering order management, credit, collections, payments, cash application, disputes and deductions, and carries tasks through until the cash is received.

Over 150 companies, from mid-market firms to Fortune 50 businesses, including Honeywell, ZoomInfo and PerkinElmer, use the platform to unlock up to 40% more cash flow and cut DSO by 47%. More than $3 billion has moved through it so far.

Founded in New York by Tarek Alaruri and Ben Winter, the company has raised $93 million from Insight Partners, Andreessen Horowitz, M12, Microsoft's Venture Fund and others. More information is at the company's site.

This is a foundational role and the first data hire, so you will help shape the data architecture and the approach to analytics. You will work with product and engineering to turn raw financial data into insights that help customers get paid faster.

Responsibilities

• Build the company's data infrastructure from scratch, including pipelines that ingest, transform and model data from customer ERPs, payment processors and internal systems.
• Create the transformation and semantic layer that gives customer-facing analytics, internal reporting and AI/ML systems one consistent set of metrics.
• Design a canonical data model that brings together data from different source systems, with quality tests and observability built in from the start.
• Build event and signal pipelines that convert product interactions and outcomes into clean, labeled data for analytics, ML and product features.
• Work with product, engineering and applied ML to build data quality, lineage and observability into every release.
• Apply DataOps practices so that data and the AI features built on it stay timely, accurate and trusted.
• Work with leadership to define KPIs, build dashboards and surface insights for strategic decisions.
• Scale the data platform as the customer base grows from dozens to hundreds, addressing bottlenecks before they arise.

Requirements

• You have 5+ years of hands-on experience building production data pipelines in Python.
• You know SQL and modern cloud data warehouses well. Experience with Snowflake or BigQuery is a plus.
• You have deep experience building ETL/ELT workflows at scale with tools such as dbt, Airflow or similar, and you have clear views on what good looks like.
• You have built or contributed to a semantic or metrics layer and care about metric consistency across surfaces.
• You understand data modeling fundamentals and can design canonical schemas that turn messy, mixed source data into usable form.
• You have worked with real-world data from SaaS APIs, ERPs and third-party integrations.
• You care about data quality and observability, treating freshness, lineage, automated testing and anomaly detection as core concerns.
• You thrive in ambiguity and enjoy building something new rather than inheriting an existing stack.
• Experience partnering with ML or applied AI teams on feature pipelines or supporting infrastructure is a bonus, not required.
• Experience in or strong interest in fintech, B2B SaaS or financial data is preferred. Understanding AR/AP workflows is a big plus.

Conditions

US compensation: top-of-market salary and an equity package.

Equal opportunity employer. The company does not discriminate on any basis protected by applicable law.

Reasonable accommodations are available during the application and interview process on request.

Benefits

• Medical, dental and vision insurance for you (U.S.-based full-time employees).
• 401(k) with match.
• Equity.
• Flexible PTO.
• Parental leave.