Top 4831 Lead engineer reports analytics Jobs

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Quick Apply
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Lead Analytics Engineer

Obsidian Security -
Palo Alto, CA

Quick Apply

Job Details

$219,000 - $240,000 a year

Qualifications

Data model design
Commercial use (data warehousing systems)
Data Integration (Data management)
Finance
Process design
AI platforms (beyond public GPTs)
Schema design
SQL
Data Architecture Design (Architecture design skills)
ETL process automation
B2B business model
Cloud database architecture
SaaS platforms
Experience with SaaS (as product category for technical sales engineers)
AI-driven automation
SaaS
Project stakeholder communication
Data analytics technologies
Dimensional modeling
Database software proficiency

Full Job Description

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 CrowdStrike, Okta, Cylance, and Carbon Black. Now, they're 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.

About the Role

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.

What You'll Do

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.

What You'll Bring

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.

Quick Apply

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lead engineer reports analytics jobs

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