Revenue Operations Data Analyst

About Wati

Started as a WhatsApp team inbox in 2020, Wati has evolved into a full revenue orchestration system that goes beyond a single platform. We empower businesses that sell, support, and grow through conversations by observing customer intent in real-time, deciding the next best revenue action, and executing it seamlessly across marketing, sales, and support—all within WhatsApp and connected messaging channels.

Our Platform & AI Capabilities

Wati is designed for scalability and intelligence. Our AI-native platform simplifies complex customer communication operations through a unified inbox, a robust multi-channel messaging infrastructure, and no-code automation. At the heart of our solution is Astra, our intelligent AI layer, which helps you create AI Agents for all customer interactions and all your messaging platforms. By integrating AI agents into the ecosystem, we enable businesses of all sizes to deliver measurable ROI and build deeper customer relationships.

Our Backing & Partnerships

Trusted by over 16,000 customers across 190+ countries, Wati is proudly backed by world-class investors including Tiger Global, Sequoia Capital, DST Global, and Shopify. As a Premium-tier Partner of Meta and Google, we maintain the highest standards of platform excellence and integration

About the Role

The Revenue Operations Analyst will be instrumental in analyzing large datasets to extract meaningful insights that impact our bottom line. You will lead the charge in optimizing our dashboards, maintaining data accuracy, and developing new data pipelines that support strategic decision-making in all aspects of our revenue stream, from sales-qualified lead to customer retention and churn.

This role requires a blend of strong technical data skills, a strategic mindset, and a thorough understanding of SaaS operations. Here is a good read of what this role requires - https://www.chargebee.com/blog/what-is-revops/\

What you’ll do:

• Business & Strategic Planning (P1): Understanding business context and building framework based on the data results in order to enable data as a decision management function to fulfill business growth appetite. Additionally, acting as the thought partner to the GTM team, in order to exploit market-fitted strategy. Contribute to strategic planning efforts by providing data-driven insights on market trends, customer segmentation, and revenue opportunities.
• Data Analysis & Reporting (P1): Utilize advanced SQL skills to query, manipulate, and analyze complex data sets within BigQuery databases to extract key customer insights that inform strategic decision-making. Translate data findings into clear, understandable reports and presentations for stakeholders across the company.
• Data Optimization (P2): Develop, refine, and optimize information flow that provide relevant insights into growth & revenue metrics, customer behavior, and performance.
• Collaboration on Data Pipelines (P2): Work closely with the data team to design and implement new data pipelines that enhance the flow and accessibility of information. Ensure that these pipelines and data structures are scalable and support the growing analytical needs of the business as well as the reports are up to date, user-friendly and provide actionable insights to drive business decisions.
• Cross-Functional Support (P1): Provide support to sales, marketing, customer success, and partnership teams by delivering tailored data insights and reports to help them meet their objectives. Act as a bridge between these departments and the data team to ensure alignment and optimal use of data.
• Process Improvement (P3): Continually assess and improve the data collection and analysis processes, incorporating best practices to enhance efficiency and accuracy. Advocate for the adoption of new tools and technologies that can improve revenue operations.
• Training and Enablement (P3): Educate and train non-technical team members on how to interpret and utilize dashboards and reports, increasing data literacy across the company.

Requirements:
• Bachelor’s degree in a business or data-related field
• 3-5 years’ work experience in a revenue operations role in a tech SaaS company
• Strong experience in SQL and querying large datasets, particularly using BigQuery.
• Strong experience with data visualization tools such as Tableau, Looker, or similar (Data science experience is a plus).
• In-depth understanding of key SaaS metrics
• Strong analytical and problem-solving skills with a keen attention to detail.
• A self-starter who is ready to take initiative and craft solutions from the ground up.
• Ability to work independently and in a fast-paced environment.