Senior Data Engineer at SailPoint

Job description

About SailPoint SailPoint is the leader in identity security for the cloud enterprise. Our identity security solutions secure and enable thousands of companies worldwide, giving our customers unmatched visibility into the entirety of their digital workforce and ensuring workers have the right access to do their job — no more, no less. Built on a foundation of AI and ML, our Identity Security Cloud platform delivers the right level of access to the right identities and resources at the right time, matching the scale, velocity, and changing needs of today's cloud-oriented modern enterprise. About the role Join the team building the SailPoint Next Gen Identity Data Platform, an industry-leading lakehouse and streaming foundation for identity security. It powers Identity Security Cloud, the Identity Graph, and analytics and ML products across SailPoint, so product teams can ingest, store, process, and serve identity data at SaaS scale. As a Senior Data Engineer, you will design, build, and operate critical pieces of that platform: batch and streaming pipelines, warehouse and lakehouse models, and the data services product teams depend on. The platform supports Identity Security Cloud, Non-Employee Risk Management, Machine Identity Security, Agent Identity Security, Data Access Security, and Cloud Infrastructure and Entitlement Management — so the shape of your data and the quality of your pipelines directly determine how fast, clear, and trustworthy those products feel to customers. This is a strong individual contributor role. You will own features and components end to end, raise the engineering bar for your team, and mentor more junior engineers. What you will do Design, build, and operate scalable ETL/ELT pipelines and data services on our managed lake-house. Own meaningful pipelines and datasets end to end — from requirements through production support. Build robust data models for the warehouse and lakehouse that support product features, analytics, and ML. Contribute to data architecture and design discussions; write and review design documents. Improve performance, reliability, and cost across Snowflake, Iceberg, Spark/Flink, and the storage and serving layers. Help keep graph-serving data models and access patterns responsive as data volume grows. Implement data quality checks, governance, lineage, and observability standards for the work you own. Partner with product, infrastructure, security, and analytics teams, and with data scientists and software engineers, to ship practical solutions. Set a strong example in code reviews and give constructive, high-signal feedback to peers. Mentor junior engineers and contribute to team practices, tooling, and onboarding. Use AI-assisted development practices to accelerate delivery while upholding code review, security, and data governance standards. Participate in the on-call rotation for data systems and turn incident learnings into lasting fixes. What you bring Required 5+ years of professional experience building production software, with substantial time in data engineering. 2+ years owning meaningful pipelines, datasets, or data services in production. Strong hands-on experience with SQL, data modeling, and query optimization — ideally on Snowflake or a comparable cloud warehouse. Strong hands-on experience with dbt and Airflow, or close equivalents. Proficiency in at least one programming language: Python, Java, or Scala. Working experience with distributed processing (batch and streaming) and production experience with at least one of Kafka, Flink, Spark, or Cassandra. Practical experience using AI-assisted software development tools (for example GitHub Copilot, Cursor, or Claude Code) in a professional engineering workflow — including data modeling, SQL/dbt, and pipeline development, testing, and documentation — with disciplined review before production use. Comfort working with cloud-native systems on AWS, Azure, or GCP. Knowledge of data governance, security, and complia