Data Analytics Engineer

Job Requisition ID # 26WD100390 Position Overview We're hiring a Data Analytics Engineer to join Customer Success Analytics (CS Analytics) within Autodesk Customer Success. This role sits at the intersection of analytics engineering, business intelligence, and insight delivery. You will build Structured Query Language (SQL) models, data pipelines, and dashboards that power retention, expansion, and growth decisions across the business. You will partner with Data Engineering, Data Science, and business stakeholders to define reliable metrics, maintain production analytics assets, and translate complex data into clear, actionable insights for Customer Success, Support, Finance, and executive audiences Responsibilities Develop production-grade Snowflake Structured Query Language (SQL) models, curated datasets, reusable metric logic, and dashboard-ready views using clean, modular engineering practices Partner with stakeholders to define metrics, improve analytics capabilities, and support pipeline delivery using dbt, refresh workflows, and orchestration Design and maintain interactive Microsoft Power BI and Tableau dashboards that communicate meaningful business insights and support executive decision-making Apply business intelligence best practices, including intuitive dashboard design, metric consistency, performance optimization, and stakeholder validation Conduct rigorous quantitative analysis across customer cohorts, retention, expansion, engagement, renewals, and business impact Serve as a subject matter expert for data structures, data quality, and metric definitions while translating analytical findings into actionable recommendations Use Python to support ad hoc analysis, data exploration, validation, automation, and quality control activities Leverage Artificial Intelligence (AI)-assisted analytics tools such as Cursor, Claude, Snowflake Cortex, and internal conversational agents to accelerate exploration, SQL development, documentation, and insight generation while validating outputs for accuracy, reproducibility, and governance Work within a Git-based version-controlled environment, following engineering best practices, peer reviews, and collaborative development standards Communicate insights effectively through dashboards, written reports, presentations, and stakeholder discussions Minimum Qualifications 3+ years of relevant experience in a Software as a Service (SaaS) environment Strong understanding of SaaS business models and key performance indicators, including retention, expansion, Annual Recurring Revenue (ARR), renewals, engagement, Net Promoter Score (NPS), Customer Satisfaction (CSAT), and utilization Strong Structured Query Language (SQL) skills with hands-on experience using Snowflake or a comparable cloud data warehouse Proven experience developing and maintaining production-grade analytics assets rather than ad hoc reporting solutions Strong experience with Microsoft Power BI and/or Tableau, including designing intuitive dashboards, developing reusable business metrics, and creating executive-ready data stories Experience applying statistical methods and hypothesis-driven analytical approaches Strong communication and storytelling skills with the ability to influence business decisions using data Experience working across Customer Success, Support, Finance, Renewals, and Product usage data domains Preferred Qualifications Experience using Python for analysis, notebooks, automation, or pipeline support Familiarity with dbt, including models, testing, documentation, and analytics engineering workflows Experience supporting Finance, Customer Success, Customer Support, Customer Service, or Renewals organizations Experience using Artificial Intelligence (AI) and Large Language Model (LLM) tools for analytics, including Cursor, Claude, Snowflake Cortex, Copilot agents, or AI-assisted SQL, while following responsible AI practices Experience with workflow orchestration tools such as Apache Airflow or