Senior Product Engineer

About Preql

Preql is building the AI-powered financial data platform for the modern enterprise. We're developing agentic AI workflows that help finance teams clean, transform, and structure their data without writing code. Think "Lovable.com for data pipelines" - just as website builders help users refine ideas into working apps, Preql helps users refine messy, fragmented financial data into production-ready workflows using natural language.

Our customers include media companies, media networks, retailers, and manufacturers who need reliable metrics and automated reporting. The company is backed by tier 1 venture firms and previous data company founders.

The Role

We're seeking a Product Engineer to own the core of our agentic workflows, starting with a data cleaning agent. You'll architect and implement an MVP that transforms how finance teams interact with their data - moving from manual Excel workflows to AI-powered, auditable data transformations.

What You'll Build:

• Agentic AI workflows that convert natural language instructions into structured data transformations

• Interactive data cleaning pipelines where users can refine transformations through conversational feedback

• Audit-ready transformation engines that show their work and generate reproducible scripts (backed by code)

• Web-based workflow builders that feel as intuitive as drag-and-drop but generate production code

• Integration harnesses for Excel, CSV, and eventually ERP systems like NetSuite and QuickBooks

Key Responsibilities:

• Design and implement the core agentic workflow engine from POC to MVP

• Build user interfaces that make complex data transformations accessible to non-technical finance users

• Create the technical foundation for LLM-powered data cleaning that maintains auditability and compliance

• Develop APIs and integrations for financial data sources

• Collaborate directly with customers to validate technical approaches and gather product feedback

• Own the full stack from data ingestion to transformation output

Requirements

Technical Foundation:

• 4+ years building production software with modern web technologies (TypeScript, Python, etc.)

• Experience with LLM APIs (OpenAI, Anthropic) and agentic workflow patterns

• Familiarity with data processing frameworks, ETL/ELT workflows, and data transformation tools and techniques

• Understanding of cloud infrastructure (AWS, GCP, or Azure)

Product Mindset:

• Track record of taking features from concept to production in fast-moving environments

• Experience gathering user feedback and translating it into technical requirements

• Understanding of data workflows and analytics concepts

• Comfort working directly with enterprise customers

Finance/Compliance Awareness:

• Understanding of audit requirements and data lineage needs

• Familiarity with financial data formats and common transformation patterns

• Appreciation for regulatory compliance in enterprise environments

Strong Advantages

Nice-to-haves, but not strict requirements.

• 0→1 Experience: Evidence of manifesting ideas into product

• AI/ML Experience: Building products with LLMs, prompt engineering, or agentic workflows

• Data Platform Experience: Working with Snowflake, DataBricks, or similar modern data stacks

• Financial Software: Hands-on experience with NetSuite, QuickBooks, HubSpot, or ERP systems

• Enterprise Sales: Customer-facing experience with technical sales or solution engineering

• Compliance Background: SOC 2, audit trail requirements, or regulatory data handling

What Makes This Role Special

You'll be building the foundational technology that defines how AI transforms enterprise data workflows. This isn't about incremental improvements to existing tools - you're creating entirely new interaction patterns between humans and data systems.

Impact: Your code will directly determine whether finance teams can trust AI with their most critical data

Growth: Join as a founding engineer with significant equity and leadership opportunities

Innovation: Work at the cutting edge of agentic AI applied to real business problems