Marketing Data Science Lead

New
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UpGuardCybersecurity SaaS
Sydney, Brisbane, Hobart, Melbourne (Australia); CA, MD, MA, IL, OR, WA, CO, TX, FL, PA, LA, MO, or DC (United States)Full-TimeLead
Salary not disclosed
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Job Details

Experience
4+ years
Required Skills
SQLBusiness IntelligenceData AnalysisBigQuerydbtLooker

Requirements

  • 4+ years delivering analytics solutions for marketing teams within a high-growth SaaS company.
  • Strong knowledge of channel logic across paid, organic, lifecycle, partner, and field marketing.
  • Fluency in attribution methodologies including multi-touch, incrementality, and marketing mix modelling.
  • Demonstrated ability to translate ambiguous requirements into well-structured dbt models.
  • Experience in a consulting role managing diverse projects and stakeholder groups.
  • Strong understanding of modern data infrastructure (BigQuery, ETL/ELT tools, dbt, ThoughtSpot, Looker).
  • Exceptional business acumen with the ability to identify key performance indicators and drive business value.
  • Excellent verbal and written communication skills for technical and non-technical audiences.
  • Direct experience in creating BI infrastructure and hands-on dbt model authorship.
  • Fluency in leveraging AI/LLM tools to accelerate analysis and code development.

Responsibilities

  • Generate actionable insights from complex marketing data sets to inform channel investment and pipeline strategy.
  • Partner with marketing leaders across demand gen, partner, field, product, and brand functions.
  • Design and maintain the attribution framework including first/multi-touch and incrementality testing.
  • Develop metrics, models, and dashboards for paid, organic, content, lifecycle, partner, and field marketing.
  • Construct foundational marketing data assets and dbt models to create a governed semantic layer.
  • Provide robust self-service and agentic analytics capabilities to marketing stakeholders.
  • Conduct technical deep-dive analysis into marketing challenges like lead quality and campaign cannibalisation.
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