Lead Analytics Engineer

Obsidian Security is the leading SaaS security platform, trusted by global enterprises like Snowflake, T‑Mobile, and Algolia. We protect 200+ organizations across North America, Europe, the Middle East, Southeast Asia, Australia, and New Zealand, including many of the world’s largest Fortune 1000 and Global 2000 companies.

Founded in 2017 and backed by top investors like Greylock, Obsidian was built to close a critical gap: securing SaaS apps where business happens—Microsoft 365, Salesforce, and hundreds more. The company does this by offering a complete SaaS security platform to reduce risk, detect and respond to threats, and prevent breaches at the source. Obsidian was built by leaders who redefined endpoint and identity security at Crowd Strike, Okta, Cylance, and Carbon Black.

Now, they’re transforming how SaaS is secured.

With global momentum, a growing partner ecosystem including Sentinel One, Databricks, and Google Cloud, and a major fundraise ahead, Obsidian is scaling rapidly toward long‑term growth and IPO readiness.

We're hiring a Lead Analytics Engineer to be the senior technical owner of Obsidian's data warehouse and analytics foundation. You will own the DBT project, the warehouse architecture, and the semantic layer that every executive dashboard, GTM workflow, and internal AI agent will rely on. You will also help lead our use of AI in how we build, using modern AI coding tools to ship dbt models, automated workflows, and reporting pipelines at a pace a small team alone could not.

We are looking for a Senior Technical owner to set the bar for mart design, documentation, AI‑augmented development, and for how data flows from our systems of record into the reports and workflows that run the business.

You will report directly to the VP of Business Systems, Data & IT. You will partner closely with Product, Sales, CS, Finance, Marketing, and Security, and you will collaborate with teammates across the Business Systems, Data & IT function.

Own the analytics foundation

• Build and own the dbt project end-to-end - mart architecture, modeling conventions, materialization patterns, testing, lineage, documentation.
• Design dimensional models that serve both human dashboards and downstream AI agents with equal rigor.
• Stand up CI/CD discipline on the dbt project, including PR review, automated testing, and deployment workflows.

Build the pipelines and the warehouse

• Own Fivetran ingestion across our GTM, finance, HR, and operational sources.
• Design and operate reverse-ETL pipelines that move trusted data from the warehouse back into systems of record.
• Migrate existing reports from our legacy database onto the new Big Query foundation without breaking continuity.

Use AI to ship more, faster

• Build with AI coding tools (Claude, Cursor, Codex, and similar) as part of your daily workflow, and set the team standard for AI-augmented analytics engineering.
• Design schemas, documentation, and semantic conventions specifically with downstream AI agents in mind - context-rich, well-named, unambiguously documented.
• Build automated workflows on top of the warehouse: cost aggregation across vendor APIs, automated executive reporting deliveries, automatic field updates from call intelligence into CRM, and similar.

Raise the bar across the team

• Set technical standards for the data function, mentor and uplevel teammates, and establish conventions that scale beyond your own hands.

Requirements

• 8+ years building analytics or data engineering systems in production, with deep, hands‑on dbt experience. You have owned a DBT project, not just contributed to one.
• Strong business fluency in B2B SaaS, and an instinct to start from the process, not the metric. You understand the upstream GTM, billing, and customer‑lifecycle workflows that generate ARR, NRR, churn, CAC, gross margin, pipeline conversion, and deal velocity.
• You can take a finance or Rev Ops question, walk the process it lives in, and translate it into the right data model. You know that the upstream process matters at least as much as the model that reports on it.
• Expert SQL and dimensional modeling. You can defend a modeling…