Data Solutions Engineer - Standard Metrics

Standard Metrics is an AI-driven financial data platform that helps investors and their portfolio companies make more informed, forward-looking decisions with automated reporting and benchmarking tools. We’re a team of optimistic product builders on a mission to accelerate innovation in the private markets. Standard Metrics is backed by 8VC and Spark Capital, along with other leading VCs and angels, and is currently a trusted partner for many of the top VC/PE firms in the world.

Come Build With Us

The Data Solutions Engineer sits at the intersection of Customer Experience and Engineering at Standard Metrics. You'll be the dedicated technical owner for customer-facing data workflows, integrations, and AI-driven automation, while working across a book of customers to understand their data problems and build solutions that make Standard Metrics stickier and more impactful in their day-to-day workflows.

This is not a pure engineering role, and it's not a pure customer-facing role. You'll need both the technical depth to architect and execute solutions across APIs, SQL, data pipelines, and AI tooling, as well as the communication skills to work directly with customers to diagnose needs, set expectations, and deliver results. You'll be deeply embedded with our Data Solutions and Customer Success teams, acting as a technical extension of the customer.

This role is a foundational hire for a nascent services function at Standard Metrics. There's no established playbook here - you'll help write it. If you're energized by ambiguity, by the prospect of defining what "technical services" looks like for a product like ours, and by building something that didn't exist before, this is that opportunity.

What You’ll Do

• Partner directly with customers to identify data challenges and design technical solutions - connecting data sources, configuring integrations, automating recurring workflows, and deploying custom reports
• Deploy and optimize AI-powered tools and workflows for customers; educate customers and internal teams on prompt engineering, LLM capabilities, and best practices for leveraging AI in their data operations
• Build and extend internal tooling (importers, parsers, and reporting pipelines) to reduce manual burden on the Customer Experience team and improve platform reliability
• Execute bespoke data operations such as custom SQL reports, bulk data operations, and backend queries for customers with unique data needs
• Own API schemas, ingestion cadence, error handling, Snowflake data shares, and database connections
• Identify repeatable patterns across customers and translate them into product requirements, filing tickets and partnering with Engineering to productize solutions