Senior Data Analytics Engineer (Remote)

Optro

8d•Remote

About The Position

We are seeking a Senior Data Analytics Engineer to build and scale the enterprise data foundation that powers our Post-Sales organization and Product Engineering teams. This role sits at the center of Customer Success, Renewals, Professional Services, Support, Customer Education, and Product, ensuring that trusted, well-governed data enables smarter decisions across the entire customer lifecycle. You will design and own scalable data pipelines, optimize data models, and establish governance standards that improve retention forecasting, risk identification, operational efficiency, product telemetry insights, and AI enablement. This is a high-impact opportunity to shape how AuditBoard leverages customer and product data to drive growth, protect revenue, and accelerate innovation.

Requirements

Bachelor’s degree in Computer Science, Engineering, or related field
5+ years of experience in data engineering, analytics engineering, or similar role within a SaaS environment
Strong expertise in SQL and modern data stack technologies (Snowflake, dbt and Tableau)
Experience building scalable ETL/ELT pipelines and data models across multiple business systems
Familiarity with CRM (Salesforce), CS tools (Gainsight), and product telemetry environments (Amplitude / Pendo), Support (Zendesk)
Strong cross-functional collaboration skills with the ability to translate business requirements into technical solutions
Experience working in high-growth SaaS environments
Understanding of SaaS metrics including NRR, GRR, ARR, retention forecasting, and customer health scoring

Responsibilities

Design, build, and maintain scalable data pipelines that ingest and transform data across Salesforce, Gainsight, Rocketlane, Zendesk, Snowflake, product telemetry, and other core systems
Strategic partner with Customer Ops & Analytics, Enterprise Data Engineering and Product Operations teams to be a thought leader to design scalable data solutions.
Develop reliable data models that power renewal forecasting, churn prediction, customer health scoring, professional services metrics, support performance, and product usage
Implement CI/CD, data testing, and documentation best practices to ensure scalable, secure, and auditable transformations
Optimize warehouse performance and data architecture for speed, cost efficiency, and reliability
Partner with Product and Engineering to design telemetry schemas and ensure meaningful product instrumentation, while building pipelines that translate product usage data into actionable insights for both Product teams and Post-Sales teams
Architect data structures that support AI/ML use cases including churn prediction, renewal forecasting, workflow automation, and sentiment analysis
Implement monitoring and validation frameworks to proactively detect data inconsistencies
Familiarity with using Python for basic to advanced data processing tasks
Develop and document data or system models, flow diagrams and architecture guidelines