• Build and maintain feature marts for machine learning pipelines, working closely with our Data Scientist to ensure production-ready data models • Design and implement data transformations using DBT, creating reliable, well-documented data pipelines from multiple source systems • Create and manage datasets that enable self-service analytics across Product, GTM, and Customer Success teams • Develop dashboards and reports in QuickSight for key stakeholders, translating business questions into actionable insights • Perform ad-hoc analyses to support product and business decisions, moving quickly to answer critical questions • Ensure data quality and reliability through testing, documentation, and monitoring • Collaborate across teams to understand data needs and deliver solutions that scale

• Architectural Overhaul: Lead the design and execution of a massive migration from 1,200+ legacy dbt models into a modular, three-project Medallion structure (Bronze, Silver, Gold). • Systems Governance: Define the "Engine Standard" for data contracts, naming conventions, and testing frameworks. You aren't just following a process; you are building the process others will follow. • Performance & Scalability: Partner with the Principal Engineer to optimize our Snowflake footprint. You will identify systemic inefficiencies in our DAGs and warehouse usage to drive down costs while increasing query speed. • Technical Mentorship: Act as a force multiplier for the team. You will lead high-level design reviews, provide "Staff-level" feedback on PRs, and elevate the technical bar for our Senior and Mid-level engineers. • Strategic Partnership: Bridge the gap between Engineering and Business. You’ll ensure our infrastructure supports long-term growth (3–5 years out) without compromising the day-to-day execution speed of our analysts.

• Build and maintain scalable data models in dbt that support reporting and analysis across Company Pillar teams • Develop well-structured, reusable datasets in Snowflake • Build and maintain reporting assets in Looker to enable self-serve analytics • Support internal teams by improving data definitions, reliability, and usability • Assist with root-cause analysis of data issues and implement sustainable fixes • Strengthen documentation around data models, definitions, and requirements • Grow your analytics engineering toolkit through peer collaboration and learning within HubSpot’s AE community

• Develop and maintain ETL processes to support data ingestion, transformation, and integration. • Work with various file formats (TXT, CSV, XML, JSON, YAML) to ensure seamless data processing. • Implement and optimize ETL workflows using EasyMorph, Databricks, Azure Data Factory, Talend, and Apache Nifi. • Write clean, efficient, and maintainable code using Python, C#, or Java. • Ensure data quality, accuracy, and integrity across ETL pipelines. • Collaborate with data engineers and analysts to define and refine ETL requirements. • Troubleshoot and resolve ETL failures, ensuring minimal downtime and impact. • Follow test-driven development (TDD) methodologies to improve reliability and maintainability. • Maintain and document ETL processes, providing insights and recommendations for improvements.