FinOps & Product Analytics Engineer

In this role you will design and maintain data models and AI-powered tooling that transform operational data into actionable insights for engineering, product, and finance teams. You will build ingestion pipelines, develop AI-powered reporting, and surface analytics via dashboards as the platform scales to new data domains. You’ll work with cross-functional experts to ensure reliable data, prompt-driven AI outputs, and clear stakeholder outcomes. This is a growth-focused role with FinOps at core and expansion into product analytics.

• competitive base pay
• performance-based variable pay
• Equity/Stock in Intapp
• opportunity to travel to other development centers
• one-time home office stipend
• generous paid parental leave and other leave policies

• Design and maintain analytical data models (schemas, views, aggregations) for FinOps data including cloud cost, allocation, anomalies, usage, and unit economics
• Build ingestion and normalization pipelines from diverse sources (AWS/Azure billing, SaaS, product telemetry, internal systems)
• Develop AI-powered reporting in Python using MCP to answer stakeholder questions (cost anomalies, spend breakdowns, usage trends)
• Implement prompt engineering and output validation to ensure trustworthy AI responses
• Create and maintain dashboards in Metabase for FinOps metrics and future domains
• Collaborate with FinOps, product managers, and domain leads to translate requirements into accurate data models and queries
• Contribute to architecture decisions as new data domains/sources are added
• Document data models, AI skill patterns, and pipeline logic for system-wide understanding
• Continuously improve data quality, query performance, and AI reliability as volume/variety/scope grow

• SQL with complex aggregations, window functions, large dataset joins
• Python for data transformation and tooling (not just notebooks)
• Experience with analytical/columnar databases (ClickHouse, BigQuery, Redshift, DuckDB)
• Proven experience with LLM APIs (Anthropic, OpenAI) and building reliable AI outputs
• Ability to translate requirements into data models and queries
• Comfort in greenfield environments and making architectural decisions
• Familiarity with MCP or similar function-calling/tool-use patterns for LLMs (preferred)

• Cross-functional collaboration
• Strong problem-solving and inquisitive mindset
• Attention to data quality and reliability
• SQL
• Python
• Analytical/columnar databases (ClickHouse, BigQuery, Redshift, DuckDB)