The Team:

You'll join the Data & Transformation (DAT) team, part of the Strategic Intelligence & Business Partner pillar. DAT is responsible for elevating data-driven decision-making across Finance and our go-to-market functions. This is an engineering role embedded close to the business: you'll write real code and build data products, but you'll also sit with the stakeholders you serve. We're raising the technical and analytical bar of the function — moving from descriptive reporting toward predictive, prescriptive, and AI-augmented insights — and we want people who help push that standard higher.

The Role:

We're looking for a highly analytical Analytics Engineer to strengthen how the team turns financial and operational data into insight. Working alongside senior analysts and business partners, you will build models, analyses, and automated data products that support smarter decisions and better performance across Finance, adoption, and GTM operations. This is a hands-on, technical role for someone who is genuinely strong with data — SQL, Python, and statistical thinking — who writes clean code, and who enjoys being close to the business and setting a higher standard of analytical and technical best practices.

The work you’ll do:

Analytics & Business Support

You'll partner with stakeholders across Finance and GTM operations to translate business questions into structured analyses and clear, well-reasoned recommendations. You'll help build the narratives and visuals that make complex data understandable, and contribute to the analytics priorities the team sets each quarter.

Advanced Analytics & Modeling

You'll build and maintain robust data and financial models, forecasting logic, and scenario analyses to support planning and decision-making. You'll develop and refine KPIs and performance metrics that give clear visibility into business drivers, and apply statistical methods and predictive modeling to anticipate trends and surface risks and opportunities early. We expect a high level of analytical rigor: sound methodology, clean assumptions, and results you can defend.

Data Products, Automation & AI

You'll build scalable, automated dashboards and data products that improve the speed and accuracy of insight, helping move the team from manual reporting toward automated, self-service analytics. You'll write clean, reproducible, version-controlled code (SQL and Python), help improve and maintain existing analytics, and make use of modern AI and agentic tooling — LLM-based assistants and coding copilots (e.g. Cursor), AI agents, and workflow automation — to accelerate work and reduce manual effort. You'll also help maintain data quality, consistency, and governance across datasets.

Cross-functional Collaboration

You'll work closely with Finance, adoption, and GTM operations, as well as Data/Tech teams, to identify analytics opportunities and embed data-driven thinking into core business processes — acting as a connection point between the business and the data.

The qualifications you need:

• 2–4 years of experience in analytics engineering, data/business analytics, BI development, or a comparable technical and analytical role
• Strong SQL skills and confidence working with large, complex datasets
• Hands-on experience with Python for data analysis, automation, and statistical/predictive modeling — a core part of the role
• Solid foundations in data modeling and/or financial modeling, forecasting, and scenario planning
• Working knowledge of statistical methods and an analytical mindset grounded in sound methodology and attention to detail
• Familiarity with software engineering best practices (version control, testing, documentation) and BI / data visualization tools
• Ability to translate data into clear insights and communicate them effectively to non-technical stakeholders
• Comfortable in a fast-paced, cross-functional environment
• Degree in Computer Science, Engineering, Mathematics, Statistics, Finance, Economics, or a related quantitative field
• Exposure to modern AI and agentic tools (LLM assistants, coding copilots such as Cursor, AI agents, prompt-based automation) and interest in applying them to analytics workflows
• Exposure to Finance or GTM/revenue operations data