Job description

Established in 2021, Independence Pet Holdings is a corporate holding company that manages a diverse and broad portfolio of modern pet health brands and services, including insurance, pet education, lost recovery services, and more throughout North America. We believe pet insurance is more than a financial product and build solutions to simplify the pet parenting journey and help improve the well-being of pets. As a leading authority in the pet category, we operate with a full stack of resources, capital, and services to support pet parents. Our multi-brand and omni-channel approach include our own insurance carrier, insurance brands and partner brands. ABOUT CLARUS Clarus, a Doubtless company, is a pioneering pet health technology company transforming the future of pet healthcare. We develop connected solutions across the pet health landscape, helping veterinary teams and pet owners make more informed care and financial decisions. Together, we are reducing complexity and helping more pets receive the care they need. ABOUT THE ROLE Clarus is seeking an experienced Analytics Engineer to join the Analytics team. This individual will own the analytics engineering process and serve as the technical bridge between Data Engineering and Analytics delivery. They will build, expand, and optimize Clarus’s analytics architecture and pipelines, improving how data flows from diverse sources to downstream analytics solutions. In this role, you will lead high-impact data product and solutions initiatives from ambiguous problem definition through technical design, implementation, rollout, and adoption. You will transform raw and curated data into reliable, analytics-ready models and consistent business metrics using dbt, SQL, Python, and Databricks, while establishing a scalable architecture and strong engineering practices. The ideal candidate is a seasoned analytics engineer with strong data-wrangling skills and a passion for building and optimizing scalable analytics systems. They are self-directed, comfortable supporting multiple projects, systems, and products, and able to set technical direction while remaining deeply hands-on. WHAT YOU’LL DO 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. WHAT YOU BRING 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. Experience in animal health, veterinary services, pet insurance, SaaS, fintech, or health technology is a plus. 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. WHAT WILL HELP YOU SUCCEED 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. LOCATION This is a remote U.S. position aligned to Eastern or Central Time business hours. U.S. travel may be required. Compensation: Chicago/Boston Base pay range: $123,774 – $154,718 per year NYC Base pay range: $135,026 - $168,784 per year Miami Base pay range: $118,148 - $147,686 Nationwide Base pay range: $112,522 - 140,653 per year This is the range we reasonably expect to pay at hire. Placement within the range depends on job-related factors such as experience, skills, and certifications. This position is eligible for an annual bonus. Base pay is one part of a total rewards package that includes the benefits listed below. All of our jobs come with great benefits including healthcare, parental leave and opportunities for career advancements. Some offerings are dependent upon the location of where you work and can include the following: Comprehensive full medical, dental and vision Insurance Basic Life Insurance at no cost to the employee Company paid short-term and long-term disability 12 weeks of 100% paid Parental Leave Health Savings Account (HSA) Flexible Spending Accounts (FSA) Retirement savings plan Personal Paid Time Off Paid holidays and company-wide Wellness Day off Paid time off to volunteer at nonprofit organizations Pet friendly office environment Commuter Benefits Group Pet Insurance On the job training and skills development Employee Assistance Program (EAP) Interview Technology Notice: Please note that phone and video interviews or screenings may be recorded and transcribed using interview technology to support our recruitment process. By continuing with the interview, you consent to this use. Text Messaging Notice: If you provide a mobile phone number, you may receive job-related communications via text message. Message and data rates may apply. You may opt out of text communications at any time by replying “STOP.”

Requirements

Must-haves

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.
Bachelor’s degree or higher in computer science, data engineering, analytics, information systems, mathematics, or a related discipline, or equivalent professional experience.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

sqlpythonci/cd

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

Paid Time Off

Parental Leave

Wellness