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Preql is a data infrastructure company helping enterprises clean, unify, and govern messy internal data for AI, analytics, and reporting. As a Forward Deployed Engineer, you will be embedded in customer environments, learning how finance organizations close their books and plan their years, then building semantic data models that make those processes work reliably.
This role bridges customer needs and product development. You will own business outcomes for a portfolio of enterprise accounts from kickoff through production and expansion. Your responsibilities include building semantic models for finance logic (revenue recognition, cost allocation, GL hierarchies, headcount planning), integrating and mapping data across ERPs, planning systems, and data warehouses, and facilitating working sessions with controllers, FP&A leads, and customer data teams to translate between finance language and data models.
You will make judgment calls on whether issues are modeling problems, source data problems, or product gaps, routing each appropriately. You will provide steady, specific product feedback grounded in real customer friction rather than anecdotes. You will create reusable models, templates, and documentation that reduce time-to-value for subsequent accounts.
Success metrics: within 90 days, move an account from install to first trusted output and trace any number back to source; within 6 months, measurably reduce time-to-first-value for comparable accounts through models and assets you built; within 12 months, own the delivery playbook, drive expansion conversations, and enable new hires to ramp on your documentation.
Required: 5+ years building with data in production, deep SQL fluency, Python comfort, hands-on experience with cloud warehouses (Snowflake, Databricks, BigQuery) and transformation tools (dbt or equivalent), and real working knowledge of financial data including chart of accounts, allocations, and close processes. You must have direct experience working with enterprise customers, including scoping, pushback, and early delivery of difficult news. High tolerance for ambiguity is essential; early accounts will lack playbooks and you will write them.
Strong signals include familiarity with ERP and planning systems (NetSuite, Workday, SAP, Oracle), background in consulting or solutions architecture at data/AI companies, experience with regulated buyers, being a first or second technical hire on a customer-facing team, or time spent inside finance or accounting functions.
