Revenue Operations Senior Analyst – Emea

Legora Legora · Vertical AI · Stockholm, Sweden · Growth

Legora is seeking a Revenue Operations Senior Analyst to scale their Demand Generation engine. This role is data-centric, focusing on designing and operating structured datasets within Salesforce and enrichment tools like Clay. The analyst will ensure data integrity, implement logic for targeting and routing, build funnel visibility, and continuously refine data models for scalability. The ideal candidate is analytical, structured, detail-oriented, and has experience in GTM/RevOps within B2B SaaS.

What you'd actually do

  1. You’ll design and operate structured datasets that power our top-of-funnel engine. That means translating GTM use cases into clean data models, defining fields, keys, joins, and logic so workflows run without friction.
  2. You’ll work directly with Salesforce objects (Leads, Contacts, Accounts, Campaigns), maintaining lifecycle integrity and ensuring relationships are accurate and reliable. Data hygiene isn’t a cleanup task here, it’s core infrastructure.
  3. You’ll implement signal thresholds, inclusion and exclusion logic, and routing readiness rules. You’ll QA data before it reaches BDRs or Sales. When something looks off, you won’t patch it, you’ll fix the root cause.
  4. You’ll build structured funnel visibility from signal to pipeline. Conversion, velocity, leakage, you’ll make it measurable and reliable. When Demand Gen has questions, the answer comes from well-modeled data, not guesswork.
  5. And as we scale, you’ll continuously refine schemas, simplify joins, and reduce manual work so the engine gets stronger, not heavier.

Skills

Required

  • analytical
  • structured
  • detail-obsessed
  • reasoning directly about CRM data models and structured datasets
  • understand lifecycle logic, object relationships
  • operationalised workflows from data

Nice to have

  • SQL
  • advanced spreadsheet skills
  • enrichment tools
  • experience in high-growth B2B SaaS environments

What the JD emphasized

  • data hygiene isn’t a cleanup task here, it’s core infrastructure
  • fix the root cause
  • well-modeled data, not guesswork