Senior Analytics Engineer

Clarus is a pet health technology company developing connected solutions that help veterinary teams and pet owners make informed care and financial decisions. The Senior Analytics Engineer will lead analytics engineering, build and optimize scalable data architecture and pipelines, and transform data into reliable analytics-ready models and governed business metrics using dbt, SQL, Python, and Databricks.

Responsibilities:

• Own analytics engineering work, including its roadmap, priorities, standards, and operating practices
• Design and evolve analytics architecture, with clear responsibilities and quality expectations across bronze, silver, and gold data layers
• Build, maintain, and optimize dbt models that transform source data into curated, reusable, analytics-ready datasets
• Write and optimize advanced SQL, and use Python for data transformation, validation, automation, and complex processing where appropriate
• Develop dimensional and business-oriented data models that support dashboards, self-service analysis, operational reporting, and data products
• Establish and document shared KPI definitions, calculation logic, data contracts, ownership, and authoritative sources
• Implement automated tests, source freshness checks, documentation, lineage, and monitoring to make data quality visible and dependable
• Establish disciplined development practices, including Git-based version control, code review, CI/CD, and controlled promotion across environments
• Partner with Data Engineering on ingestion, source schemas, platform performance, and reliable handoffs between foundational and analytical data layers
• Partner with analysts, BI developers, and business stakeholders to translate requirements into scalable models rather than one-off reporting logic
• Improve Databricks performance and cost efficiency through thoughtful table design, incremental processing, query optimization, and compute usage
• Lead data-quality investigations, root-cause analysis, remediation, and communication when critical analytics assets do not meet expectations
• Strengthen data governance by applying appropriate access controls, handling sensitive data responsibly, and supporting consistent ownership and stewardship
• Migrate duplicated spreadsheet, dashboard, and ad hoc query logic into governed, tested, and reusable dbt models
• Design resilient models that support historical analysis, changing source systems, incremental loads, and evolving business definitions
• Create clear technical documentation, modeling conventions, and enablement materials that drive adoption of trusted data assets
• Raise analytics engineering capability across the team through design reviews, reusable patterns, thoughtful feedback, and hands-on mentorship

Requirements:

• Bachelor's degree or higher in computer science, data engineering, analytics, information systems, mathematics, or a related discipline, or equivalent professional experience
• A strong record of success leading complex analytics engineering, data engineering, business intelligence engineering, or closely related initiatives
• 8+ years of relevant data and analytics experience, including senior-level ownership of production analytics data models and standards
• Advanced SQL skills and demonstrated experience designing, debugging, and optimizing complex transformations
• Deep, hands-on dbt experience, including scalable model architecture, testing, incremental models, snapshots, documentation, macros, packages, and production deployment workflows
• Strong Databricks experience, including Delta Lake, Spark SQL, Workflows or Jobs, Unity Catalog, and performance management / tuning
• Demonstrated skill operating medallion architectures and lakehouse data models across bronze, silver, and gold layers
• Strong Python skills for data processing, testing, validation, automation, and engineering utilities
• Strong understanding of dimensional modeling, warehouse and lakehouse design, semantic layers, slowly changing dimensions, and metric governance
• Experience with modern engineering practices, including Git, CI/CD, code review, automated testing, orchestration, and environment management
• Ability to translate ambiguous business needs into clear technical designs and communicate effectively with senior leaders and non-technical stakeholders
• You are intellectually curious, resourceful, and motivated to understand how the business works
• You take initiative, identify needs early, and you are comfortable operating in an evolving, high-growth environment
• You can move easily between hands-on analytics engineering, and stakeholder conversations
• You can manage production data deliverables while continuing to improve the underlying processes
• You build trust by producing reliable data products, communicating clearly, and following through on commitments
• Experience in animal health, veterinary services, pet insurance, SaaS, fintech, or health technology is a plus