Obsidian Security

Lead Analytics Engineer

Palo Alto, CA

Sponsorship not specified$219k-$240kDetected 49 days ago

PythonSQLBigQuerySnowflakeRedshiftDatabricksGCPCI/CDOAuthAirflowdbtData EngineeringAgentic AICybersecuritySalesforceMarketoCRMTest Automation

About the role

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

You have owned a DBT project, not just contributed to one.
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.
You can defend a modeling decision in a paragraph, and you have opinions about incremental materialization.
You can explain a data model decision to an engineer and a stakeholder with equal clarity.
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).
In compliance with federal law, all persons hired will be required to submit satisfactory proof of identity and legal authorization.

Production experience with a cloud data warehouse (BigQuery preferred
Snowflake, Redshift, or Databricks transferable) and a managed ELT platform (Fivetran preferred).

securing SaaS apps where business happens-Microsoft 365, Salesforce, and hundreds more.
Obsidian was built by leaders who redefined endpoint and identity security at CrowdStrike, Okta, Cylance, and Carbon Black.
Now, they're transforming how SaaS is secured.
Major announcements are on the horizon.

Our competitive benefits packages are designed to support our employees' well-being, both at work and at home.
Competitive compensation with equity and 401k
Comprehensive healthcare with dental and vision coverage
Flexible paid time off and paid holiday time off
12 weeks of new parent or family leave
For more details on our US benefits, or for information on our international benefits, please see here.
In addition to a competitive base salary, this position is eligible for equity awards and may be eligible for sales commission or incentive compensation based on the role or function within the company.

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
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.
At Obsidian, we are proud to be an equal-opportunity employer.

If you have a need that requires accommodation, please contact accommodations@obsidiansecurity.com

This listing is sourced directly from Obsidian Security's careers page and normalized into a canonical job model.