Senior Analytics Engineer

Senior Analytics Engineer

Company

Reap

Conditions

6 days ago

Skills

Candidate Availability

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

You will own business domains from underlying data and definitions through dashboards and stakeholder conversations. You will deliver commercial analyses, identify growth and margin opportunities, maintain trusted metrics and dashboards, enable stakeholder self-service, and collaborate on data modeling, ingestion, priorities, and rollout.

Requirements

• 8+ years of analytics engineering or analytics experience
• End-to-end ownership of business-domain data, metrics, and reporting
• Analytical judgment for ambiguous business questions
• Experience partnering with commercial, finance, or risk teams
• Expert SQL
• Dimensional modeling
• Experience building or maintaining semantic or metrics layers
• Experience with a cloud data warehouse and BI tooling
• Financial-data validation, reconciliation, and root-cause analysis
• Documentation and training for non-technical stakeholders
• Experience generating pricing, profitability, or growth insights that influenced commercial strategy, investment decisions, or client proposals

Responsibilities

• Own business domains from data and definitions through dashboards and stakeholder conversations
• Deliver pricing, cost-saving, forecasting, client deep-dive, and proposal analyses
• Identify revenue opportunities, growth levers, and margin-expansion opportunities
• Translate analyses into commercial recommendations for business owners
• Partner with business teams as embedded decision support
• Turn recurring questions into data columns, dashboards, and documentation
• Design and maintain vetted views and metric definitions in the Snowflake semantic layer
• Build and maintain Metabase dashboards
• Validate and reconcile dashboard figures against Finance-owned figures
• Provide documentation, training sessions, office hours, and Slack support
• Collaborate with Data Engineering on ingestion and modeling decisions
• Collaborate with Data Product Managers on priorities and rollout

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