• 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.

• Lead complex product pipeline builds: Design and develop scalable data pipelines supporting cross-domain product launches in Payments, Credit, and Subscriptions, from scoping through delivery • Collaborate cross-functionally: Partner with Product Analysts and Product Managers to define data requirements, while analysts continue owning pipelines alongside you • Build product reporting infrastructure: Create reliable, performant marts and models that power product dashboards and self-service analytics • Design scalable data models: Apply modeling frameworks (one big table, entity tables, Kimball) tailored to product analytics use cases • Optimize and monitor: Maintain pipeline health, optimize query performance, and implement data quality monitoring • Enable the team: Document solutions, establish best practices, and help upskill Product Analysts on pipeline development • Integrate cross-functional data: Work across domain boundaries to unify product data into cohesive reporting structures

• Own the Data Ecosystem: Build and maintain robust data pipelines and ETL processes • Design and manage data warehouses to support advanced analytics, focusing on creating 'Gold Standard' data models • Maintain comprehensive documentation of all data engineering processes • Design and manage data lakes, warehouses, and databases to support advanced analytics and AI workflows • Act as a SQL/Python expert to optimize data pipelines and troubleshoot issues

• Own the Transformation Layer: Design, build, and maintain scalable data models using dbt and SQL to support diverse business needs, from monthly financial reporting to near-real-time operational metrics. • Set Technical Standards: Establish and enforce best practices for data modelling, development, testing, and monitoring to ensure data quality, integrity (up to cent-level precision), and discoverability. • Enable Stakeholders: Collaborate directly with finance, operations, customer success, and marketing teams to understand their requirements and deliver reliable data products. • Integrate and Deliver: Create repeatable patterns for integrating our data models with BI tools and reverse ETL processes, enabling consistent metric reporting across the business. • Ensure Quality: Champion high standards for development, including robust change management, source control, code reviews, and data monitoring as our products and data evolve.