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

Solutions Engineer – Media Generation

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