Preql helps enterprises clean, unify, and govern messy internal data so it actually works for AI, analytics, and reporting. We work with large organizations navigating complex data environments and high-stakes operational workflows. Based in New York, our team comes from data infrastructure, AI, and enterprise software.
We’re a small team with little bureaucracy. Leadership expects individuals to take ownership, move quickly, and make good decisions for the company with support from their teammates. The curious do well here, are comfortable operating in ambiguity, and are willing to form opinions and act on their convictions instead of waiting for instructions.
You will sit inside customer environments, learn how a specific finance organization actually closes its books and plans its year, and build the semantic models that make that work. You will be the person who understands both a customer's GL and our platform internals well enough to get the numbers right and defend them to a controller.
This is not a support role and it is not pure services. Every deployment you run should make the next one faster. The work you do by hand in month one should be a product capability by month six. You will be the loop between what customers need and what we build.
The business outcomes for a portfolio of enterprise accounts, from kickoff through production and expansion
Semantic models for finance logic: revenue recognition, cost allocation, GL and cost center hierarchies, headcount and driver based planning
Source integration and mapping across ERPs, planning systems, and warehouses, including the reconciliation problems that surface once real data lands
Working sessions with controllers, FP&A leads, and customer data teams, translating between finance language and data models
The judgment call on what is a modeling problem, a source data problem, or a product gap, and routing each one to the right place
A steady stream of product feedback backed by specifics, not anecdotes, so engineering builds against real customer friction
Reusable models, templates, and documentation that shrink time to value on every subsequent account
90 days: you have taken an account from install to first trusted output, and you can explain any number in a customer's reporting back to its source
6 months: time to first value for a comparable account has dropped measurably because of models and assets you built, and customers ask for you by name
12 months: the delivery playbook is yours, expansion conversations start with work you did, and the next engineers we hire ramp against what you wrote
5+ years building with data in production, with deep SQL fluency and comfort in Python
Direct experience with cloud warehouses (Snowflake, Databricks, BigQuery) and transformation tooling (dbt or equivalent)
Real working knowledge of financial data. You know why the finance team's definition of revenue is different from the data team's, and you have modeled a chart of accounts, an allocation, or a close process before
Experience working directly with enterprise customers, including the parts that are uncomfortable: scoping, pushing back, and delivering bad news early
High tolerance for ambiguity. Early accounts will not have a playbook, and you will write the playbook
Judgment about when to solve something for one customer and when to solve it for all of them
Familiarity with ERP and planning systems (NetSuite, Workday, SAP, Oracle)
Background in consulting, solutions architecture, or professional services at a data or AI company
You have worked with regulated buyers
You have been the first or second technical hire on a customer facing team
Time spent inside a finance or accounting function, or close enough to one to have felt a close
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