Staff Data Engineer

Staff Data Engineer – Streaming Data Systems

Location: REMOTE
Team: Data Platform
Level: Staff

About the Role

We are looking for a Staff Data Engineer with deep expertise in streaming data architectures to drive the evolution of our real-time data platform. This role is highly technical and hands-on — ideal for someone who thrives on solving complex scaling challenges, architecting low-latency systems, and leading teams to deliver high-quality streaming data solutions that power analytics, machine learning, and core product features.

As a senior member of the data engineering team, you’ll shape the technical direction of our streaming ecosystem and help define best practices for how data flows through our organization.

Responsibilities

• Architect and lead the design and implementation of scalable, fault-tolerant streaming data pipelines using platforms such as Apache Kafka, Flink, Spark Structured Streaming, or Kinesis.

• Evolve the real-time data platform to support new business use cases, ensuring low-latency data delivery and high reliability.

• Partner with data scientists, product engineers, and platform teams to design data models and streaming APIs that enable real-time insights and decision-making.

• Drive data quality, observability, and governance across streaming systems, including schema management, lineage, and monitoring.

• Optimize pipeline performance, reliability, and cost across multiple environments (cloud and on-prem).

• Lead code reviews, architecture reviews, and technical mentoring for other engineers.

• Establish and champion best practices for streaming data engineering, deployment automation, and CI/CD for data systems.

• Collaborate with platform teams to ensure data systems are secure, compliant, and highly available.

Qualifications

Required:

• 8+ years of experience in data engineering, with at least 3+ years in real-time or streaming systems.

• Deep expertise with Kafka (or equivalent message brokers), including schema registry, partitioning strategies, and consumer group management.

• Hands-on experience with stream processing frameworks (e.g., Apache Flink, Spark Structured Streaming, Kafka Streams).

• Strong proficiency in Python, Scala, or Java, and familiarity with data pipeline orchestration tools (Airflow, Dagster, etc.).

• Solid understanding of distributed systems, networking, and storage.

• Experience with cloud platforms (AWS, GCP, or Azure) and managed streaming services (Kinesis, Pub/Sub, etc.).

• Familiarity with data modeling, lakehouse architectures, and batch + streaming integration.

Preferred:

• Experience building exactly-once or idempotent streaming pipelines.

• Knowledge of data governance, lineage, and observability tools (e.g., Datadog, Prometheus, OpenLineage).

• Prior experience leading a data platform or mentoring senior engineers.

• Exposure to machine learning feature pipelines or event-driven microservices.

What You’ll Bring

• A strong sense of ownership and curiosity for optimizing data flow in complex environments.

• Ability to think strategically while delivering practical, scalable solutions.

• Leadership in driving cross-team collaboration and influencing architectural direction.

• Passion for helping teams move from batch-oriented thinking to event-driven design.

Why Join Us

You’ll play a pivotal role in building the next generation of our data platform, enabling real-time insights that directly impact business outcomes. We invest in engineering excellence, autonomy, and continuous learning — and we’re looking for a Staff Data Engineer who shares that vision.

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 o