• Collaborate with team members to collect business requirements, define successful analytics outcomes, and design data models while considering non-functional requirements such as performance constraints, cost constraints, and security policies. • Model, design, develop, test, and implement appropriate backend and front-end structures needed to meet business visualization and reporting requirements. • Configure SaaS tools such as ELT providers, integration platforms, and orchestration tools to meet system requirements to build a modern cloud data warehouse that is accurate, reliable, high-performing, and easily accessible to our business departments. • Provide day-to-day support of the data warehouse and associated data marts, including establishing trusted data sets for common data with supporting documentation. • Coordinate with the platform data engineering team, data analyst teams, data governance teams, and IT teams to align with change management processes and to ensure performance processes and access is optimal for global reporting solutions. • Improve processes regarding data flow and quality to improve data accuracy, viability, and value including testing small chunks of data transformations for correspondence to the set tasks. • Understand business/department needs and goals and create data-driven analyses and insights to support management decisions.

• Own end-to-end internal ELT data pipelines: from API ingestion all the way to self-serve dashboards. • Build and maintain canonical data models and metric definitions (for product adoption, usage, and marketing metrics). • Build dashboards and shared analytics tools to support decision making, data-driven content, and self-serve BI. • Collaborate with Product, Marketing, and other internal teams to translate business questions into data requirements. • Ensure data quality, consistency, and governance of data pipelines.

• Work with clients as a participant in their BigQuery and Looker analytics journey • Understand client needs from an organizational, process, and technological perspective • Lead interactive working sessions and conduct hands-on workshops with client technical teams • Design, implement, and maintain data models in Looker, ETL pipelines, and build actionable dashboards

• Architect, design, and lead the implementation of highly complex, scalable, and resilient data solutions in the cloud, leveraging AWS, Snowflake, dbt, Fivetran, and other modern technologies. • Be the Expert. Quickly build subject matter expertise in a specific business area and data domain. Understand the data flows from creation, ingestion, transformation, and delivery. • Support defining and executing the overarching strategy for the analytics engineering function, including the development and evangelization of data frameworks, standards, and best practices across the organization. • Lead efforts in designing, building, and maintaining a robust, governed, and scalable semantic layer to provide consistent and reliable data access for business intelligence and analytics. • Spearhead the technical vision and roadmap for data quality and governance, establishing frameworks and processes to ensure data integrity and proactively address systemic issues. • Act as a primary technical consultant to senior executives and business stakeholders, translating complex data concepts into actionable insights and strategic recommendations. • Mentor, coach, and develop junior and mid-level analytics engineers, fostering a culture of technical excellence, innovation, and continuous learning within the team. • Set standards for documentation, conduct advanced peer code reviews, and define comprehensive testing strategies for data solutions. • Continuously evaluate and champion new technologies and methodologies to enhance the data and analytics capabilities at Huntress.