• Translate ambiguous business questions into structured analytical frameworks • Develop KPI definitions, metric layers, and standardized reporting logic across teams • Partner with Product and Growth teams to define success metrics and measurement strategies • Deliver deep-dive analyses on ordering behavior, churn risk, marketplace performance, and operational efficiency • Present insights and trade-offs clearly to senior stakeholders and leadership • Own and scale A/B testing infrastructure, including experiment tracking and evaluation pipelines • Define best practices for hypothesis testing, power analysis, sequential testing, and causal inference • Build experimentation dashboards and automated reporting systems • Partner with Data Science to productionize predictive models and design reusable ML-ready datasets • Support feature engineering and monitoring for production ML systems • Design and maintain scalable ELT/ETL workflows that power analytics, experimentation, and ML use cases • Build and optimize dimensional data models (star schemas, data vault, etc.) to enable self-service analytics • Create trusted, well-documented datasets for product, finance, operations, and growth teams • Improve data quality, observability, lineage, and governance across the analytics ecosystem • Optimize performance across warehouse and transformation layers • Drive best practices in analytics engineering across the organization • Influence tooling decisions (Airflow, dbt, Snowflake, Spark, etc.) with scalability and business usability in mind • Mentor analytics engineers and elevate modeling and measurement standards • Champion a data-driven culture grounded in experimentation and measurable impact

• 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