Sr. Revenue Operations Data Engineer (Hybrid in Bangalore)

<div class="content-intro"><p>For over 20 years, Smartsheet has helped people and teams achieve–well, anything. From seamless work management to smart, scalable solutions, we’ve always worked with flow. We’re building tools that empower teams to automate the manual, uncover insights, and scale smarter. But more than that, we’re creating space– space to think big, take action, and unlock the kind of work that truly matters. Because when challenge meets purpose, and passion turns into progress, that’s magic at work, and it’s what we show up for everyday.</p></div><p>Smartsheet is looking for an experienced Data Engineer to support and accelerate the productivity of our Go-To Market organization. In this role, you will work with Data Engineers to help build scalable and reliable data and reporting infrastructure. You will partner with Analytics decision-makers as part of a collaborative, motivated team. Your work will be instrumental in helping our Sales partners optimize their pipeline, increase retention, and close deals.</p> <p>Our ideal candidate is curious and displays an ability to translate abstract data requests into actionable data solutions . To be successful in this role, you are able to communicate to a diverse audience of internal stakeholders. You should have strong technical acumen with data analytics tools and languages, the ability to identify areas of opportunity within the business, and build innovative solutions.</p> <p><strong>You Will:</strong></p> <ul> <li>Help to create, evolve, and maintain the reporting infrastructure for the Sales organization to facilitate data across the organization</li> <li>Partner with engineering, data science, and other analytics teams to build scalable analytics solutions with a focus on reporting capabilities</li> <li>Mine large datasets to ensure data accuracy and completeness for reporting purposes </li> <li>Analyze and monitor data for anomalies, with a focus on data quality and consistency within reports </li> <li>Develop and maintain key performance indicators (KPIs) to track sales performance, pipeline health, and other critical metrics </li> <li>Automate report generation and distribution to improve efficiency</li> <li>Provide technical expertise and guidance to junior analysts on data extraction, transformation, and loading (ETL) processes and reporting best practices</li> <li>Ensure data governance and data integrity across all reporting tools and platforms</li> <li>Proactively identify and propose improvements to existing reporting processes and systems</li> <li>Be a mentor and leader to the more junior analysts on the team</li> <li>Perform other duties as assigned</li> </ul> <p><strong>You Have:</strong></p> <ul> <li>Bachelor's degree in a quantitative field (e.g. engineering, sciences, math, statistics, business, or economics)</li> <li>5+ years of experience in an analytics/engineering role with increasing responsibility</li> <li>Experience in Cloud and SaaS go-to-market (GTM)</li> <li>Expertise in SQL and preparing data for analysis, modeling, and visualization</li> <li>3+ years of experience using Python for data analysis / automation </li> <li>3+ years of experience with dbt core</li> <li>3+ years of experience with Git (Gitlab, Github etc.) and CI/CD concepts like branching, merging, Code Reviews etc. </li> <li>Demonstrated experience with a modern engineering tech stack (Airflow, AWS, Docker etc.) </li> <li>Experience working with a business intelligence tool like Tableau, PowerBI, Looker, etc.</li> </ul> <p><strong>Nice To Have:</strong></p> <ul> <li>Experience building ML models</li> <li>Proficiency with Tableau preferred</li> <li>Experience with statistics (time series analysis, cluster analysis, multivariate analysis)</li> <li>Experience publishing and presenting data and findings to executives and non-data users</li> </ul><div class="content-conclusion"><p> </p> <p><strong>Get to Know Us:</strong></p> <p>At Smartsheet, your ideas are heard, your potential ... (truncated, view full listing at source)