About this role
Every function here - growth, operations, product, client success runs on the same warehouse. When a model breaks, a definition drifts, or a dashboard goes stale, decisions across the company degrade quietly. We need an analyst who owns that layer: builds it in dbt, keeps it trustworthy as the logic underneath changes, and turns it into visibility people actually use.
This is not a report-pulling job. It is equal parts data engineering discipline and business judgement - you will be asked what the number means, not just what it is.

Key Responsibilities
Build and maintain scalable dbt models across staging, intermediate, and smart layers with proper testing, documentation, and version control.
Maintain and improve data products as business requirements evolve, including RevOps logic changes across models, schemas, dashboards, and MIS reporting.
Own data structures and warehouse design for assigned domains, ensuring scalability, data freshness, quality and cost efficiency.
Maintain data accuracy and representation across key datasets, dashboards, MIS reports,



and RevOps data flows.
Perform SQL-based RCA across data models and key org metrics (business, growth, AI, and Voice Agent KPIs) to identify trends, root causes, performance gaps, and revenue-impacting opportunities.
Automate recurring reports, analytics workflows, and setup alerts to reduce manual data work through scalable pipelines.
Work with cross-functional teams to translate business questions into explicit requirements, analyses, and timely insights.

Requirements
1+ years of relevant experience in Analytics, BI, or Data Engineering roles.
Strong SQL skills with experience writing and optimizing queries on large relational databases.
Strong analytical and problem-solving skills with the ability to understand complex data, schemas, ETLs, and business logic.
Experience building dbt models for warehousing that integrate data across new and legacy infrastructure.
Experience investigating RCAs and

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