[Hiring] FinOps & Product Analytics Engineer @Intapp

Role Description

We are seeking a curious and technically sharp FinOps & Product Analytics Engineer to join our DevOps & Cloud Platform team. In this role, you will build the data foundation and AI-powered tooling that turns operational data into clear, actionable insight for engineering, product, and finance stakeholders across Intapp.

We are starting with FinOps — cloud cost, cost allocation, anomaly detection, and usage visibility — but this role is designed to grow. As we expand into product usage analytics and additional data domains, you will help shape how we ingest, model, and surface that data through both AI-powered chat and dashboards. Subject matter experts guide the requirements; your job is to build the “how”: reliable data models, AI-powered reporting tools, and analytics layers that teams actually rely on.

What You Will Do:

• Design and maintain analytical data models — schemas, views, and aggregations — that correctly represent FinOps data including cloud cost, cost allocation, anomaly signals, infrastructure usage, and unit economics, with additional domains to follow.
• Build ingestion and normalization pipelines that clean and unify data from heterogeneous sources — AWS and Azure billing, SaaS platforms, product telemetry, and internal systems — into a consistent, queryable analytical layer.
• Develop AI-powered reporting skills in Python using MCP (Model Context Protocol) that return structured, reliable answers to stakeholder questions — covering cost anomalies, team-level spend breakdowns, usage trends, and more as new domains come online.
• Implement prompt engineering and output validation layers that make AI-generated answers trustworthy — handling empty results, ambiguous time ranges, and conflicting data gracefully.
• Build and maintain dashboards in Metabase covering FinOps metrics (showback, chargeback, anomaly signals, unit costs) and expanding to additional domains as the platform grows.
• Collaborate with FinOps experts, product managers, and other domain leads to translate requirements into accurate data models and query patterns that reflect how the underlying data actually behaves.
• Help evaluate and contribute to architecture decisions as new data domains and source systems are brought into the platform.
• Document data models, AI skill patterns, and pipeline logic so the broader team can understand, extend, and trust what you build.
• Continuously improve data quality, query performance, and AI output reliability as the platform scales in volume, variety, and scope.

Qualifications

• Strong SQL skills — complex aggregations, window functions, joins across large datasets, and the instinct to investigate when a number looks wrong before shipping.
• Python proficiency — clean, maintainable scripts for data transformation, normalization, and tooling (not just notebooks).
• Experience with analytical or columnar databases — such as ClickHouse, BigQuery, Redshift, DuckDB, or similar.
• Demonstrated experience building with an LLM API (Anthropic, OpenAI, or similar) — you understand tool use, structured outputs, and what it takes to make AI responses consistent and reliable.
• Ability to work from well-defined requirements: translating a subject matter expert’s question into a data model and query that accurately answers it.
• Comfort in a greenfield environment — able to make sound architectural decisions when patterns are still being established.
• Familiarity with MCP (Model Context Protocol) or similar function-calling / tool-use patterns for LLMs (preferred).

Benefits

• Attractive compensation – including competitive base pay and performance-based variable pay.
• Equity/Stock in Intapp.
• Opportunity to travel to other development centers for product training and cross-site collaboration.
• One-time home office stipend.
• Generous paid parental leave (including adoptive leave), marriage leave, bereavement leave, carer's leave, and paid sick days.
• Reimbursement for training towards continuing education.