SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Standard Metrics is an AI-driven financial data platform serving top VC/PE firms with automated reporting and benchmarking tools. The Data Solutions Engineer is a foundational hire for a nascent technical services function, sitting at the intersection of customer experience and engineering.
You'll be the dedicated technical owner for customer-facing data workflows, integrations, and AI-driven automation. This hybrid role requires both deep technical expertise and strong communication skills. You'll partner directly with customers to identify data challenges and design solutions—connecting data sources, configuring integrations, automating workflows, and deploying custom reports.
Key responsibilities include: deploying and optimizing AI-powered tools for customers; building and extending internal tooling to reduce manual burden on the Customer Experience team; executing bespoke data operations such as custom SQL reports and bulk data operations; owning API schemas, ingestion cadence, error handling, Snowflake data shares, and database connections; identifying repeatable patterns across customers and translating them into product requirements; supporting internal engineering workflows by triaging quality-of-life bugs; and carrying a light book of data parsing work to stay grounded in day-to-day operations.
You'll need 3–5+ years in a technical role with customer-facing components (solutions engineering, data engineering, technical account management). Required skills: strong SQL for complex queries and ad-hoc analysis; Python for scripting and automation; REST API design and consumption; familiarity with modern data infrastructure (Snowflake, dbt); and genuine curiosity about AI with experience in prompt engineering or LLM workflows preferred. B2B SaaS, fintech, or private markets experience is ideal. You must be willing to travel up to 60% of the time for on-site customer work. High ownership mentality and comfort operating in ambiguity are essential—you'll help define what technical services looks like for the platform.
