Data Solutions Engineer

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. The Data Solutions Engineer will serve as the technical owner for customer-facing data workflows and integrations, collaborating with customers to solve data challenges and build impactful solutions.

Responsibilities:

• 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
• Support internal engineering workflows as a secondary function by helping to triage and resolve quality-of-life bugs and enhancements that are too small for core Engineering but directly impact the Customer Experience team and customers
• Carry a light book of data parsing work alongside your teammates to stay grounded in the team's day-to-day workflows and build supporting solutions

Requirements:

• 3–5+ years of experience in a technical role with a customer-facing component - solutions engineering, data engineering, technical account management, or similar
• Strong SQL skills and ability to write complex queries, perform ad-hoc data analysis, and work with relational data models
• Proficiency in Python for scripting, data manipulation, and workflow automation
• Hands-on experience with REST APIs: designing, consuming, and debugging integrations
• Willingness to travel up to 60% of the time to work on-site with customers
• Familiarity with modern data infrastructure such as Snowflake, dbt, or similar data warehouse and pipeline tools
• Strong communication and ability to translate technical concepts clearly for non-technical stakeholders
• Experience in B2B SaaS, ideally in fintech, venture capital, or private markets
• High ownership mentality. You identify problems proactively and drive solutions without waiting to be asked
• Confidence operating in ambiguity. You don't need a fully defined playbook to get started, and you're energized rather than unsettled by the prospect of helping build one
• Experience with prompt engineering, LLM-based workflows, or AI-forward tooling is a strong plus
• Bonus: experience with data integration tools (Zapier, n8n, Make, or similar), exposure to fund accounting and investment data workflows, or experience configuring MCP servers and AI agents
• Finance and/or Computer Science degree strongly preferred