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 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.

Preferred

• 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.

Success looks like

• First 30 days - Onboard, learn the product and stack, meet stakeholders, set up environments, and land scoped reliability, performance, modeling, or pipeline-hardening work.
• By 90 days - Own meaningful features or pipelines end to end; contribute to design and reviews; start mentoring on dbt, Snowflake, and pipeline practice.
• By 6 months - Trusted owner of one or more platform areas with measurable impact; improve reliability, freshness, or cost with before/after metrics; share on-call; occasionally debug escalations that have stalled others; strengthen reviews, tooling, mentorship, and docs.

Benefits and Compensation listed vary based on the location of your employment and the nature of your employment with SailPoint.

As a part of the total compensation package, this role may be eligible for the SailPoint Corporate Bonus Plan or a role-specific commission, along with potential eligibility for equity participation. SailPoint maintains broad salary ranges for its roles to account for variations in knowledge, skills, experience, market conditions and locations, as well as reflect SailPoint's differing products, industries, and lines of business. Candidates are typically placed into the range based on the preceding factors as well as internal peer equity. We estimate the base salary, for US-based employees, will be in this range from (min-max, USD):
$108,600 - $183,018.00
Base salaries for employees based in other locations are competitive for the employee's home location.

Benefits Overview

1. Health and wellness coverage: Medical, dental, and vision insurance

2. Disability coverage: Short-term and long-term disability

3. Life protection: Life insurance and Accidental Death & Dismemberment (AD&D)

4. Additional life coverage options: Supplemental life insurance for employees, spouses, and children

5. Flexible spending accounts for health care, and dependent care; limited purpose flexible spending account

6. Financial security: 401(k) Savings and Investment Plan with company matching

7. Time off benefits: Flexible vacation policy

8. Holidays: 8 paid holidays annually

9. Sick leave

10. Parental support: Paid parental leave

11. Employee Assistance Program (EAP) and Care Counselors

12. Voluntary benefits: Legal Assistance, Critical Illness, Accident, Hospital Indemnity and Pet Insurance options

13. Health Savings Account (HSA) with employer contribution