> Markdown version of [/jobs/ext/1949784-lead-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/1949784-lead-analytics-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Analytics Engineer - **Company:** Obsidian Security, Inc. - **Location:** Palo Alto, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Sql Data Warehouse, Artificial Intelligence, Data Analysis, Automation of Tests, Backup Devices, BigQuery, Continuous Integration, Customer Data Management, Information Engineering, Extract Transform Load (ETL), Data Warehousing, Dimensional Modeling, BIG-IP Global Traffic Manager (GTM), SQL Databases, Data Streaming, Data Ingestion, Delivery Pipeline - **Published:** August 6, 2026 - **Apply:** https://us.experteer.com/career/view-jobs/lead-analytics-engineer-palo-alto-ca-usa-58817837 ## About the Role and * Design and operate reverse-ETL to move trusted data back to systems of record * Migrate legacy reports to the new BigQuery foundation without disrupting continuity * Apply AI tools in daily workflows and define standards for AI-augmented analytics engineering * Create schemas, documentation, and semantic conventions optimized for downstream AI agents * Build automated workflows on the warehouse for cost aggregation, executive reporting, and CRM data updates * Set technical standards, mentor teammates, and establish scalable conventions Tasks * 8+ years in production analytics or data engineering with hands-on dbt ownership * Strong business fluency in B2B SaaS and understanding of GTM, billing, customer lifecycle workflows (ARR, NRR, churn, CAC, gross margin, pipeline) * Ability to translate finance/RevOps questions into data models, understanding upstream processes * Expert SQL and dimensional modeling with opinions on incremental materialization * Production experience with a aaa to data warehouse (BigQuery preferred) and a managed ELT (Fivetran preferred) * Demonstrated daily use of AI coding tools to deliver production output * Strong written and verbal communication skills Key requirements * ## Description Experteer Overview In this role you lead the analytics foundation as the senior owner of Obsidian's data warehouse and DBT-driven analytics. You will own the dbt project, warehouse architecture, and semantic layer that power executive dashboards, GTM workflows, and internal AI agents. You'll drive AI-enabled development to ship models, workflows, and reporting pipelines at a pace a small team could not achieve alone. Your work shapes mart design, documentation, and data flows from systems of record into business reports, with close collaboration across Product, Sales, Finance, Marketing, and Security. Compensation / Benefits * Own the end-to-end dbt project: mart architecture, modeling conventions, materialization patterns, testing, lineage, and documentation * Design dimensional models serving dashboards and AI agents with rigor * Establish CI/CD for dbt: PR reviews, automated testing, and deployment workflows * Manage data ingestion with Fivetran across GTM, finance, HR, and operations * Design and operate reverse-ETL to move trusted data back to systems of record * Migrate legacy reports to the new BigQuery foundation without disrupting continuity * Apply AI tools in daily workflows and define standards for AI-augmented analytics engineering * Create schemas, documentation, and semantic conventions optimized for downstream AI agents * Build automated workflows on the warehouse for cost aggregation, executive reporting, and CRM data updates * Set technical standards, mentor teammates, and establish scalable conventions Tasks * 8+ years in production analytics or data engineering with hands-on dbt ownership * Strong business fluency in B2B SaaS and understanding of GTM, billing, customer lifecycle workflows (ARR, NRR, churn, CAC, gross margin, pipeline) * Ability to translate finance/RevOps questions into data models, understanding upstream processes * Expert SQL and dimensional modeling with opinions on incremental materialization * Production experience with a cloud data warehouse (BigQuery preferred) and a managed ELT (Fivetran preferred) * Demonstrated daily use of AI coding tools to deliver production output * Strong written and verbal communication skills Key requirements * ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [Making Data Warehouses fast. 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