Data Architect

Independence Pet Group

3d•Hybrid

About The Position

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. Clarus is a pioneering pet health technology company on a mission to transform the future of pet healthcare. We are dynamic entrepreneurs improving pet health by providing trusted and transparent information across the entire pet health landscape. Join us in shaping a smarter, healthier future for pets everywhere. As we expand, we are looking for a seasoned Data Architect, in the data team, who can hit the ground running by building data models across the operational and analytics data store. This role will be reporting to the Senior Director of Data and Analytics. Role Overview This is a hands-on role for an experienced Data Architect with a proven track record of implementing a scalable data lake/medallion architecture and building robust data models in partnership with our product and engineering teams. The role is focused on designing and implementing scalable data solutions using Azure Databricks, acting as a subject matter expert in the platform to create efficient data pipelines, manage data lakes, and ensure data quality while leveraging the full capabilities of the Azure cloud ecosystem.

Requirements

Bachelor’s degree in computer science, Information Technology, or related field.
At least 10 years of experience in data architecture and data modeling.
Proven experience with Databricks, Spark, and Azure data services.
Solid understanding of Lakehouse architecture, Modern Data Warehousing, and Delta Lake concepts.
Experience designing and maintaining config-driven ETL/ELT pipelines with support for Change Data Capture (CDC) and event/stream-based processing.
Proficiency with NoSQL and SQL databases.
Strong understanding of data modeling, schema design, and database performance optimization
Practical experience working with various file formats, including JSON, Parquet, and ORC.
Familiarity with machine learning and AI integration within the data platform context.
Hands-on experience building and maintaining CI/CD pipelines (Azure DevOps, GitLab) and automating data workflow deployments.
Solid understanding of data governance, lineage, and cloud security (Unity Catalog, encryption, access control).
Strong understanding of data governance and data quality principles.
Excellent problem-solving and analytical skills.
Experience with data modeling tools and techniques.
Knowledge of cloud-based data solutions and platforms.
Must be authorized to work in the U.S.
Hours are flexible, but availability during core business hours (Eastern) is expected.

Nice To Haves

Preference individuals that are located in Boston, NYC, Chicago or Cleveland.

Responsibilities

Design and implement data architecture solutions using Databricks, Spark, and Azure data services.
Develop and optimize data models for both operational and analytical systems.
Ensure data integrity, security, and accessibility across various platforms.
Integrate data pipelines with event-based and microservice architectures leveraging Service Bus, Event Hub, and Functions.
Create and maintain data standards, guidelines, and best practices.
Conduct data quality assessments and implement data governance policies.
Provide guidance on data storage solutions and ELT/ETL processes.
Collaborate with developers, analysts, and data scientists to deliver trusted datasets for reporting, analytics, and machine learning use cases.
Participate in architecture discussions, backlog refinement, estimation, and sprint planning.
Contribute to defining and maintaining best practices, coding standards, and quality guidelines for data engineering.
Perform code reviews, provide technical mentorship, and foster knowledge sharing within the team.
Continuously evaluate and enhance data engineering tools, frameworks, and processes in the Azure environment.