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Salary: USD 120,000 - 130,000 / annual
Obsidian Security, a unicorn-status cybersecurity company ($1.1B valuation, Series D funded), is building a new Service Operations function within Customer Success. You will own the reporting and automation layer for the Customer Success organization, serving as the primary consumer of data built by Business Systems and transforming it into automated reporting, AI-assisted tools, and executive dashboards that enable customer health tracking, streamline QBRs and EBRs, and provide clear visibility into customer outcomes.
Key responsibilities include:
• Own the reporting and automation layer for Customer Success — translate stakeholder needs for account health, QBR/EBR decks, and executive dashboards into field-level requirements, then build the reporting and automation on top of the Customer Success data mart.
• Default to AI-first workflows — shape the roadmap for both deterministic (data plumbing) and non-deterministic (AI/LLM-driven) automation that surfaces customer insights, health checks, and recurring reports, replacing manual, ad hoc work wherever possible.
• Automate recurring Customer Success workflows beyond reporting, including renewal/adoption tracking, repetitive support tasks like ticket triage and diagnostics, and cross-platform automation that connects support tools (e.g., Zendesk) with systems like Salesforce, Slack, and the data warehouse to cut down manual handoffs.
• Help define a secure, auditable data access model for Customer Success workflows, in partnership with Business Systems and Security.
• Maintain data quality and documentation for Customer Success data sources, flagging gaps, inconsistencies, or governance issues.
• Partner across Business Systems, Product/Engineering, and Customer Success (TAM and Support) to shape how data flows into the reports and tools people use every day.
This is a foundational role at a high-growth, rapidly scaling company with strong momentum, a growing partner ecosystem (SentinelOne, Databricks, Google Cloud), and fresh Series D capital behind it.
Requirements:
• Hands-on experience with Databricks (or a comparable modern data platform), including querying, data modeling on governed marts, and metric/semantic views.
• Familiarity with Python or another scripting language for data transformation and automation.
• AI-first approach to problem-solving — you default to evaluating where agentic or LLM-driven workflows can replace manual, ad hoc reporting.
• Strong communication skills — able to translate ambiguous business reporting needs into concrete data requirements.
• Understanding of data access governance and security best practices, particularly around customer-tenant data.
• Comfortable operating with ambiguity, partnering directly with engineering/business systems teams that own the broader data warehouse.
Plus (not required):
• Experience with Salesforce data and/or customer success platforms (e.g., Totango, Zendesk, Gainsight).
• Experience designing or implementing automation for technical support workflows and cross-platform integrations (e.g., ticket triage, diagnostics, connecting support tools to Salesforce, Slack, or data platforms using an established integration platform).
• Experience with cybersecurity, SaaS security, or a technical B2B SaaS environment.
