About the job
About Us
At Optro, we are proud to be the premier platform for audit, risk management, ESG, and InfoSec, having achieved over $300M in Annual Recurring Revenue. Our innovative technology is trusted by more than half of the Fortune 500, including 7 of the Fortune 10, to enhance their operational clarity and agility. Our commitment to excellence has earned us top ratings on G2.com and Gartner Peer Insights.
We foster a culture of innovation and teamwork, constantly exploring new ways to support our clients and positively impact our community. This dedication has led us to be recognized as one of the 500 fastest-growing tech companies in North America by Deloitte for seven consecutive years.
Why You'll Love This Opportunity
We are on the lookout for a Senior Data Analytics Engineer to construct and scale the enterprise data infrastructure that supports our Post-Sales and Product Engineering teams. This pivotal role collaborates with Customer Success, Renewals, Professional Services, Support, Customer Education, and Product teams to ensure that reliable and well-governed data drives informed decisions throughout the customer journey.
You will be responsible for designing and maintaining scalable data pipelines, optimizing data models, and setting governance standards that enhance retention forecasting, risk management, operational efficiency, product telemetry insights, and AI capabilities. This is a transformative chance to influence how Optro utilizes customer and product data to foster growth, protect revenue, and drive innovation.
Key Responsibilities
Design, build, and maintain scalable data pipelines that process and transform data from Salesforce, Gainsight, Rocketlane, Zendesk, Snowflake, product telemetry, and other essential systems.
Act as a strategic partner with Customer Ops & Analytics, Enterprise Data Engineering, and Product Operations teams to lead the design of scalable data solutions.
Develop dependable data models that underpin renewal forecasting, churn prediction, customer health scoring, professional services metrics, support performance, and product usage analytics.
Implement CI/CD, data testing, and documentation best practices to ensure scalable, secure, and auditable data transformations.
Enhance warehouse performance and data architecture for efficiency, cost-effectiveness, and reliability.

