Lead Analytics Engineer

Obsidian Security•Palo Alto, CA

1d•$219,000 - $240,000

About The Position

Obsidian Security, founded in 2017, is a company focused on securing SaaS applications used by modern businesses. They have developed a comprehensive SaaS security platform to mitigate risks, detect threats, and prevent breaches. The company is backed by prominent investors and its team includes leaders from well-known security companies. Obsidian is currently transforming SaaS security in the era of AI and is trusted by global enterprises. They have significant global momentum, a growing partner ecosystem, and are preparing for a major fundraise, indicating rapid scaling towards long-term growth and IPO readiness. The company is seeking individuals to join them in defining the future of SaaS security.

Requirements

8+ years of experience building analytics or data engineering systems in production.
Deep, hands-on dbt experience, having owned a dbt project.
Strong business fluency in B2B SaaS, with an understanding of upstream GTM, billing, and customer-lifecycle workflows that impact key SaaS metrics.
Ability to translate finance or RevOps questions into appropriate data models by understanding the underlying processes.
Expert SQL and dimensional modeling skills, with the ability to articulate modeling decisions and opinions on incremental materialization.
Production experience with a cloud data warehouse (BigQuery preferred; Snowflake, Redshift, or Databricks are transferable).
Experience with a managed ELT platform (Fivetran preferred).
Demonstrated daily use of AI coding tools for production work.
Strong written and verbal communication skills, capable of explaining data model decisions clearly to both engineers and stakeholders.

Nice To Haves

Python for ingestion, automation, and integration work.
Workflow automation experience on Workato, Airflow, or similar platforms.
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.
Experience operationalizing customer-data separation in a warehouse (mart access policies, anonymization pathways, privacy-aligned PII handling).
B2B SaaS or cybersecurity domain background.

Responsibilities

Own the analytics foundation, including the dbt project end-to-end (mart architecture, modeling conventions, materialization patterns, testing, lineage, documentation).
Design dimensional models suitable for both human dashboards and downstream AI agents.
Establish CI/CD discipline for the dbt project, encompassing PR review, automated testing, and deployment workflows.
Own Fivetran ingestion across GTM, finance, HR, and operational sources.
Design and operate reverse-ETL pipelines to transfer data from the warehouse back into systems of record.
Migrate existing reports from the legacy database to the new BigQuery foundation while maintaining continuity.
Utilize AI coding tools (Claude, Cursor, Codex, etc.) as part of the daily workflow and set the team standard for AI-augmented analytics engineering.
Design schemas, documentation, and semantic conventions with downstream AI agents in mind.
Build automated workflows on top of the warehouse, such as cost aggregation, automated executive reporting, and automatic field updates from call intelligence into CRM.
Set technical standards for the data function, mentor teammates, and establish scalable conventions.