AIHR's Data & Analytics team is part of Business Operations. You will work alongside Vahid, our Senior Data Engineer, and report to Leo, our VP of Business Operations. Our ingestion and warehouse foundation is largely established: data from systems such as HubSpot and our own LMS moves through ETL pipelines into Snowflake. Through special Projects, we are building governed semantic layers on top so the business can self-serve trusted analytics through Power BI, Metabase, and AI tools.
Your role owns the step that matters most: translating a business question into a clear definition, a well-designed model, and conversational/agentic analytics that your leadership trusts. You will set the analytical architecture, dbt standards, and roadmap, while Vahid focuses on ingestion, warehouse reliability, and engineering implementation. You will both build: you will personally deliver the most critical business models, review implementations, and ensure the outputs are usable by the business.
This role has no direct reports. Your colleague, Vahid, remains managed by the VP of Business Operations, while you have clear functional decision rights over metric definitions, semantic-model design, analytical quality, dbt standards, and technical priorities. It is designed for someone who wants to lead through expertise while remaining close to the work.
Own and execute a rolling one-year Data & Analytics roadmap, balancing urgent business needs with foundational work. Guide Vahid through clear priorities, design reviews, code review, and reusable modeling patterns.Lead discovery across Marketing, Sales, Customer Success, Product, and Finance to uncover the decision behind a request and translate it into agreed metric definitions, governed semantic layers, and data models that connect the full customer lifecycle.Set the analytical architecture and dbt standards; design, build, test, and document the most critical governed models yourself using SQL and dbt.Own the full-funnel measurement framework—from marketing reach, demand generation, attribution, and customer acquisition cost through lead and pipeline conversion, sales cycle, bookings, and revenue to onboarding, product adoption, expansion, renewal, churn, and customer lifetime value. Keep core recurring-revenue definitions finance-grade and reconciled while showing how upstream activity drives downstream customer outcomes.Own the logic and quality behind executive dashboards and self-service analytics across Power BI, Metabase, and AI-enabled interfaces. Present the results to senior leaders, explain every assumption, and respond constructively to challenge.Partner closely with Vahid on warehouse and pipeline requirements, resolve conflicting numbers at the source, and raise the technical quality of the team. Apply AI where it improves development speed or data access, while using sound judgment around privacy, governance, validation, and trust.A Typical Week
This is a hands-on lead role. The exact balance will shift with business priorities, but you should expect roughly half your time to remain close to the data—building, reviewing, and investigating—and the rest to be split across discovery, functional leadership, and decision support.
Design or improve a dbt model that connects data across HubSpot, our LMS, billing, or other source systems; write the SQL, tests, documentation, and metric logic needed to put it into production.Run discovery sessions with leaders in Marketing, Sales, Customer Success, Product, or Finance to clarify the decision they need to make, challenge the requested metric, and agree on the definition.Work side by side with Vahid on source-data requirements, architecture choices, and implementation; review code and unblock delivery without handing off all of the technical work.Investigate a commercial or customer-lifecycle question, improve an executive or functional dashboard, and present the conclusion—including assumptions and limitations—to the relevant leadership team.Reprioritize the Data & Analytics roadmap, resolve competing requests, and invest in the semantic layer, governance, and reusable modeling foundations that reduce future ad hoc work.Within your first three months, you have established a clear operating model with Vahid, mapped ownership of the most important metrics, agreed the Data & Analytics roadmap, and resolved at least one material conflicting-number problem.
Within six months, AIHR has a governed customer-lifecycle domain in dbt and an executive view connecting acquisition and pipeline to revenue, retention, and product usage, and has launched the first MVP of our agentic analytics platform to drive self-serve adoption. The underlying definitions are documented, reconciled where relevant, and trusted across Marketing, Sales, Customer Success, Product, and Finance.
Within twelve months, two or three priority business domains are governed end to end, leadership can trace performance from acquisition through lifetime value using consistent numbers, self-service adoption is growing, and Vahid can independently implement the modeling patterns and quality standards you established.
Throughout, you remain hands-on in SQL, dbt, Snowflake, and analytics while setting direction, coaching through the work, and earning leadership's trust in both the numbers and the roadmap.
