Lead Analytics Engineer

Obsidian Security•Palo Alto, CA

22h•Remote

About The Position

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.

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.

Nice To Haves

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.

Responsibilities

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