Senior Analytics Engineer

Independence Pet Group•Chicago, MA

1d•$112,522 - $168,784•Remote

About The Position

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. 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.

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.

Nice To Haves

Experience in animal health, veterinary services, pet insurance, SaaS, fintech, or health technology is a plus.
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.

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.