About the roleAs the Data Infrastructure Platform Manager, you will lead the team responsible for the runtime and platform layer beneath SailPoint's batch, streaming, analytics, and machine learning workloads. Your team owns the availability, scalability, security, performance, lifecycle, and cost efficiency of shared services such as Airflow, Flink, Spark on AWS EMR, Kafka, Snowflake, and Iceberg. Data and product engineering teams own the workload-specific pipelines and processing logic that run on those services.
Your day will span people leadership, production operations, technical strategy, and cross-functional execution. You will review service health and incidents, set priorities across operational and roadmap work, coach and unblock engineers, make architectural and investment tradeoffs, and partner with data engineering, Developer Platform, SRE, Observability, Security, and Infrastructure teams. You will make the platform easier to consume through paved roads, CI/CD, configuration as code, observability, automation, and self-service-all while protecting reliability, quality, and cost efficiency in a fast-moving environment.
About the teamThe Data Infrastructure Platform team designs, builds, and operates the production-grade data processing infrastructure that powers SailPoint Identity Security. We provide reliable, scalable, and secure data platforms as services so data and product engineers can focus on DAGs, streaming jobs, pipelines, models, and business logic rather than provisioning and operating the underlying infrastructure. The team values engineering and operations excellence, service ownership, practical automation, constructive debate, continuous learning, and a "strong opinions, loosely held" mindset.
Roadmap for successSuccess in this role will be measured through the following outcomes:
30 days- Build working relationships with team members and key partners across data and product engineering, Data Operations, Developer Platform, SRE, Observability, Security, and Infrastructure.
- Document and align stakeholders on the team charter, service catalog, ownership boundaries, escalation paths, and the distinction between platform ownership and workload-specific pipeline ownership.
- Complete an initial assessment of the team's people, platforms, roadmap, on-call load, incidents, operational risks, capacity, and cloud costs; share a prioritized set of immediate risks and opportunities.
- Establish a regular operating cadence for team priorities, service health, incidents, roadmap delivery, and cross-team dependencies.
90 days- Publish an outcome-oriented 12-month platform roadmap, developed with technical leads and partner teams, that balances reliability, scalability, security, developer experience, and cost.
- Define the operating model for the team's highest-criticality services, including named ownership, service-level objectives, observability expectations, on-call practices, capacity planning, lifecycle management, and incident follow-up.
- Select and begin delivery of the first high-value paved-road or self-service improvement for deploying and operating Airflow DAGs, Flink or Spark jobs, or another priority data workload.
- Set clear performance expectations and development goals for each team member, identify capability or staffing gaps, and establish a hiring and development plan where needed.
- Baseline the platform's key reliability, delivery, toil, utilization, and cost measures so subsequent improvements can be demonstrated with data.
6 months- Have service-level objectives, actionable dashboards, alerts, and recurring service reviews in place for the highest-criticality data platform services, with measurable progress against the 90-day reliability baseline.
- Deliver at least one production self-service or standardized delivery capability that reduces the effort and lead time required for developers to deploy data workloads safely across supported environments.
- Implement a cost and capacity management program with service-level visibility, accountable owners, prioritized optimization work, and documented efficiency gains.
- Strengthen incident response, change management, disaster recovery, vulnerability remediation, and operational runbooks; demonstrate reduced recurring toil or faster recovery for priority failure modes.
- Establish a clear platform approach for supporting machine learning workloads, including appropriate use of AWS SageMaker or equivalent capabilities, model lifecycle needs, and operational guardrails.
1 year- Operate the data infrastructure platform as a mature internal product with a clear service catalog, documented support model, paved roads, self-service capabilities, standardized CI/CD, and transparent reliability and cost reporting.
- Deliver the highest-priority roadmap outcomes and demonstrate measurable year-over-year improvement in platform availability, incident recovery, deployment lead time, developer effort, operational toil, and cost efficiency.
- Build a healthy, high-performing team with clear ownership, strong technical leadership, meaningful career growth, effective succession coverage, and the capability to execute both roadmap and operational work predictably.
- Establish a durable multi-year strategy for Airflow, Flink, Spark and AWS EMR, Kafka, Snowflake, Iceberg, and ML infrastructure that anticipates growth, regional expansion, security requirements, and evolving developer needs.
- Be recognized by partner teams as a responsive, reliable platform organization that enables them to deliver data products and business value faster without assuming the burden of operating shared infrastructure.
Requirements- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent professional experience.
- Proven experience managing and leading a cloud infrastructure, platform engineering, SRE, or data infrastructure team responsible for business-critical production services.
- Deep understanding of cloud infrastructure, distributed systems, and production operations, including:
- 5+ years of production experience with AWS.
- 3+ years of experience with Kubernetes and containerized workloads.
- Ability to read, review, and troubleshoot software written in Python, Go, Java, or a comparable language.
- Experience with infrastructure as code, preferably Terraform, and modern CI/CD or GitOps practices.
- Strong systems, networking, security, and distributed-systems troubleshooting fundamentals.
- Substantial hands-on experience operating production data platforms at scale, with depth in several of the following: Apache Airflow, Apache Kafka, Apache Flink, Apache Spark, AWS EMR, Snowflake, and Apache Iceberg. Experience operating these technologies as shared services-not only consuming them-is strongly preferred.
- Experience with machine learning systems and their operational lifecycle, including practical experience training, evaluating, or deploying ML models and familiarity with AWS SageMaker or an equivalent ML platform. Databricks experience is a plus.
- Working knowledge of modern LLM capabilities and AI-assisted engineering practices, with sound judgment about responsible use, validation, and production guardrails.
- Demonstrated success building internal platforms as products, including self-service developer experiences, standardized delivery workflows, CI/CD, observability, and clear service ownership.
- Strong production-operations discipline, including metrics and observability, SLOs, incident response, capacity planning, disaster recovery, and continuous reliability improvement.
- Experience managing cloud infrastructure cost, capacity, and performance, with a track record of making measurable efficiency improvements.
- Excellent leadership, communication, negotiation, and cross-functional collaboration skills, including the ability to align teams with competing goals and clarify ambiguous ownership.
- Ability to thrive in a fast-paced environment with shifting priorities and incomplete information while preserving engineering rigor, production uptime, and quality.
- Experience with Agile or similar iterative planning and delivery practices, applied pragmatically to a team that balances roadmap work with operational demand.
- Passion for engineering excellence, reliability, continuous learning, and developing people.
The Tech Stack- Cloud and infrastructure: AWS, Amazon EKS, Kubernetes, Terraform, and configuration as code.
- Data orchestration and processing: Apache Airflow, Apache Flink, Apache Spark, and AWS EMR.
- Streaming, storage, and analytics: Apache Kafka, Snowflake, Apache Iceberg, and AWS data services.
- Delivery and operations: CI/CD, GitOps, metrics, logs, traces, alerting, SLOs, and incident management.
- Software: Python, Go, Java, or comparable languages.
- AI and machine learning: AWS SageMaker or equivalent ML platforms, modern LLM capabilities, and AI-assisted engineering tools. Databricks experience is a plus.
