We’re building a modern, high-scale data platform to power real-time analytics and a next-generation Data Lake / Data Mesh architecture. Our environment handles massive throughput with Kafka processing over 1 million records per second and petabytes of total data, running on AWS EKS (Kubernetes) with a mix of on-prem and cloud components. Join a collaborative team where your work directly enables cutting-edge data streaming and processing—no on-call rotations, no mandatory certifications, and plenty of fun along the way.
Why Join Us?
Free GrubHub food deliveries to keep you fueled
Free downtown parking
Frequent team parties every 2 weeks – we celebrate wins and enjoy time together
Work on high-impact, large-scale systems without after-hours support demands
Opportunity to contribute to evolving initiatives like Data Lake, Data Mesh, Apache Iceberg, and real-time processing pipelines
What You’ll Do
As a key member of our data infrastructure team, you’ll focus on designing, deploying, and maintaining our core streaming and orchestration platforms. This is a hands-on DevOps-style role centered on reliability, scalability, and automation—primarily setup, configuration, cluster management, and operational excellence.
Lead the setup, configuration, and ongoing management of Kubernetes clusters (primarily EKS on AWS), ensuring high availability and performance for data workloads.
Own Apache Kafka operations at massive scale: create and manage topics, scale nodes/brokers, implement tagging/classification, tune for 1M+ records/second throughput, and handle petabyte-level storage.
Deploy and operate Kafka using modern tools like Strimzi (Kafka operator for Kubernetes), Confluent components, and Helm charts.
Support integration and orchestration with tools such as Apache Spark, Airflow, Apache Beam, and emerging Flink pipelines for batch and streaming workloads.
Contribute to building and enhancing our Data Lake and Data Mesh architecture, incorporating open table formats like Apache Iceberg for reliable, versioned data storage.
Collaborate with data engineers and application teams to provision resources, troubleshoot issues, and implement best practices for observability, security, and cost efficiency.
Automate infrastructure and deployments using IaC principles (e.g., Helm, Terraform where applicable) to enable self-service for teams.
No off-hours/on-call support is required—this role focuses on core-hours impact and proactive platform engineering.
What We’re Looking For
4–5+ years of real-world, hands-on experience in DevOps, platform engineering, or infrastructure roles (we value practical experience over certifications).
Strong primary expertise in AWS services, especially EKS (Kubernetes cluster management).
Solid production experience with Apache Kafka at scale—topic creation/management, cluster scaling, performance tuning, and related ecosystem tools.
Familiarity with Kafka deployment on Kubernetes via Strimzi, Confluent, or Helm.
Working knowledge of related data processing tools: Spark, Airflow, Apache Beam, and ideally exposure to Flink or similar streaming engines.
Experience or interest in modern data lake technologies like Apache Iceberg, Data Lake / Data Mesh patterns.
Comfortable in a mixed on-prem and cloud (AWS) environment.
Strong problem-solving skills, attention to detail, and a collaborative mindset—no need to be a lone-wolf rockstar.
Nice to Have (but not required):
Experience with Helm charts, operators in Kubernetes, or GitOps workflows
Exposure to Confluent Platform features
If you’re excited about high-throughput streaming, Kubernetes-native platforms, and contributing to a growing Data Mesh/Data Lake initiative—while enjoying great perks and a balanced lifestyle— we’d love to hear from you!
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