Business Intelligence Engineer, Comun

• Strong SQL, Python, dbt, and a BI/notebook tool (Hex, Looker, Mode, or similar): these are the core of the role
• 3+ years in a data analyst, analytics engineer, or similar role, ideally with direct ownership of a business domain
• Experience with a modern warehouse (Snowflake, BigQuery, Redshift, or similar)
• Some exposure to financial data (payments, banking, lending, or similar), including reconciliation and settlement concepts
• Real, demonstrable use of AI tools in your analytics workflow; come ready to name the tool, the workflow it accelerated, and the outcome
• A clear communicator who can explain a data-driven conclusion to a PM or exec without losing the nuance
• Nice to have: experience in a regulated financial product, fraud/risk analytics or credit modeling, reverse-ETL tools (Hightouch, Census), orchestration tools, or experience owning a semantic layer for LLM/agent-driven querying

Desirable

• This role focuses on delivering high-quality analytics at scale across Comun, split across two key areas:
• Designing and owning canonical data models in our warehouse, and delivering the dashboards, monitoring, and research that turn that data into decisions. You'll own specific domains end to end (e.g. reconciliation, remittances, risk): deciding what gets measured, building the pipelines and definitions behind it, and clearly framing insights to drive decision making
• Pushing the boundaries of our data platform and infrastructure to enable trusted AI outcomes on our data. As we scale, that means expanding our warehouse context and semantic layer, exploring new tools and integrations, and driving toward an AI-native warehouse and data product, with an obsessive focus on the trust and quality of outcomes generated through agentic processes
• You will join the Data team, partnering closely with Product, Growth, Risk, and Finance
• Build and maintain the dbt models and metric definitions that power analytics across the business, so every team works from the same numbers
• Build the semantic layer that gives AI agents and stakeholders the context and structure to use data reliably, turning raw domain data into trusted, reusable assets
• Own measurement, reporting, and analysis end to end for specific domain areas; decide what gets measured, how, and why
• Partner with Product, Growth, Risk, and Finance on high-stakes questions (feature impact, funnel drop-off, fraud and loss trends, cohort behavior) with rigor
• Own dashboards and self-serve tooling in Hex so stakeholders get trustworthy answers without waiting on a one-off query
• Communicate data driven findings clearly to stakeholders and leadership, without flattening the nuance or overstating what the data supports