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 worlds 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 happensMicrosoft 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 CrowdStrike Okta Cylance and Carbon Black. Now theyre transforming how SaaS is secured.

With AI driving rapid SaaS growth and complexity agentic AI tools gain privileged access to sensitive data through integrations creating new risks most security tools miss. Obsidian uniquely detects anomalous OAuth token activity and manages integration risks. Major announcements are on the horizon. Recognizing that SaaS security needs to evolve Obsidian enables growing organizations to start with a lightweight prevention-focused browser extension and expand coverage over time.

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

Were hiring a Lead Analytics Engineer to be the senior technical owner of Obsidians 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 BigQuery 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 RevOps 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 decision in a paragraph and you have opinions about incremental materialization.
• Production experience with a cloud data warehouse (BigQuery preferred; Snowflake Redshift or Databricks transferable) and a managed ELT platform (Fivetran preferred).
• Demonstrated daily use of AI coding tools to ship real work - not as a curiosity but as a tool you have used to deliver production output.
• Strong written and verbal communication. You can explain a data model decision to an engineer and a stakeholder with equal clarity.

Preferred

• Python for ingestion automation and integration work.
• Workflow automation experience on Workato Airflow or a similar platform.
• Reverse-ETL experience on Census Hightouch or equivalent.
• Hands-on familiarity with the GTM data stack (Salesforce Marketo Gong or similar).
• Experience in standing up an analytics warehouse from a greenfield state.
• Experience working on a Business Systems Revenue Operations or similar internal ops team with direct exposure to the processes that generate the data.
• Experience operationalizing customer-data separation in a warehouse (mart access policies anonymization pathways privacy-aligned PII handling).
• B2B SaaS or cybersecurity domain background.