Senior Data Engineer

SailPoint is a provider of identity security solutions for cloud enterprises. The Senior Data Engineer will build and operate the Next Gen Identity Data Platform, developing scalable data pipelines, lakehouse and warehouse models, and data services that support identity security, analytics, and machine learning products. The role also involves architecture, reliability, governance, cross-functional collaboration, mentoring, and on-call support.

Responsibilities:

• 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

Requirements:

• 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 compliance best practices
• Demonstrated ability to deliver high-quality work independently and on time, including in unfamiliar areas of the codebase
• Strong communication skills: you can write a clear design doc and explain tradeoffs to engineering and product partners
• BS in Computer Science, Engineering, or equivalent practical experience
• Background building data platforms at scale, including lakehouse patterns with Apache Iceberg
• Exposure to graph-oriented data systems and large relationship datasets
• interest in billion-scale problems is a plus
• Experience with SLOs and modern observability for data systems (Grafana, Prometheus, Datadog) and with instrumenting data jobs for performance, quality, and cost visibility
• Experience with Kubernetes, infrastructure as code (Terraform, CloudFormation), and continuous delivery for data systems
• Operational tuning experience with Spark or Flink
• Experience with search and information retrieval systems such as OpenSearch
• Exposure to machine learning workflows and MLOps practices
• Familiarity with IAM/IGA, compliance, security, or other privacy-sensitive product domains