Salesforce / HubSpot Administrator - Revenue Systems (SaaS)
Quick facts
- Type
- Part-time
- Location
- Remote
- Skills
- SQL
- Platform
- Micro1 — 260 open jobs
- Typical pay at Micro1
- $25–150/hour · median $90 (17 jobs on our site)
- Apply
- Free to apply — never pay to get AI training work
pay
Required Skills
Operations
CRM Ownership
Revenue Tools Beyond the CRM
Operational Judgment and Policy Conflicts
Data Accuracy and Reporting
About micro1
micro1 is the leading AI data lab for training frontier models and evaluating AI agents. Experts contribute their diverse subject matter knowledge across domains such as finance, healthcare, STEM engineering, and more. micro1 transforms that real-world expertise into high-quality training data, evaluations, and feedback loops that improve how AI systems learn, reason, and perform.
Our platform identifies and vets top talent through an AI recruiter, enabling high-quality expert contributions at scale. We aim to enable 1 billion people to do meaningful work by applying their expertise to AI. As our global expert network grows, micro1 is building the human intelligence layer for frontier AI.
Role Title: Salesforce / HubSpot Administrator - Revenue Systems (SaaS)
Role Type: Contractor
Location: Remote
micro1 is engaging Salesforce / HubSpot Administrator - Revenue Systems (SaaS) experts to contribute to a dynamic customer project focused on operational excellence in CRM and revenue systems.
In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.
You'll work inside a SaaS company's revenue stack where the operational logic has drifted out of alignment: rules that contradict each other, automations still firing against policy that was retired two years ago, the same customer sitting under two different legal names, permissions that don't match what the documentation says, and grey areas.
Scope of Work
Audit CRM and integrated revenue systems for contradictions, obsolete policies, duplicate records, and operational inconsistencies.
Analyze and resolve policy conflicts, deciding which business rules should take precedence and documenting clear, actionable reasoning.
Draft precise operational prompts outlining best practices for handling complex, real-world SaaS data scenarios.
Assess and reconcile underlying data to clarify the intended state across platforms and ensure consistency.
Document recommended changes as well as areas deliberately left unchanged, supported by sound operational judgment.
Evaluate and articulate the impact of proposed changes on connected tools such as billing, CPQ, ticketing, or integration platforms.
Contribute insights on system design, optimization, and sustainable CRM governance in a SaaS context.
Preferred Qualifications
4–6 years of hands-on CRM administration or implementation with Salesforce, HubSpot, Microsoft Dynamics 365, or comparable platforms.
Proven track record of real-world CRM build-outs, including implementations, migrations, consolidations, or rollouts led independently.
Extensive experience configuring fields, flows, validation rules, objects, and permission sets; certifications are appreciated but practical build experience is prioritized.
Working knowledge of at least two related revenue or operational systems (e.g., billing, subscription management, CPQ, ticketing, project tracking, integration middleware).
Comfort working in the data layer: SQL, APIs, system data models, and cross-system data reconciliation.
Background in SaaS environments, with a preference for regulated industries or multi-product complexity.
Excellent professional English communication skills, both written and verbal, especially in documenting technical reasoning and operational policy decisions.
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About Micro1
micro1 recruits engineers and domain experts for AI labs using its AI interviewer. Once vetted, contractors work on human-data projects such as writing and checking model outputs or building coding evaluations.