Senior Software Engineer, Data Platform
What you will be doing
You will design, develop, and operate a secure, scalable, reliable, and efficient platform to facilitate the flow of data between internal and external systems supporting mechanical and electrical engineering, supply chain, inventory, manufacturing, and finance. This is a high-impact, high-visibility role: together with a cross-functional team owning business critical applications, you will multiply the impact of many other teams across the company. You will report directly to our VP of Engineering at our Everett, WA office.
You will:
Design and develop large-scale, batch and real-time data pipelines, ELT processes, data warehouses, and data lakes, both on prem and in the cloud
Scale our data platform to support operational growth from millions of parts per year to millions per day
Monitor and continuously improve the performance and reliability of data connections and their impact on up-and-downstream systems, using best-in-class observability tools
Ensure data quality and governance by managing metadata like schemas, lineage, and access control
Collaborate with other software engineers, data analysts and scientists, other engineering disciplines, non-engineers, leadership, and vendors to understand data requirements and deliver solutions
Stay current with emerging data technologies while recommending battle-tested right-fit solutions that align with strategic goals
Participate in on-call rotations to maintain our SLA with the rest of the company, and prevent data issues from stalling 24/7 production schedules
Required Skills:
Bachelor’s degree in computer science or a related field
8+ years of software and/or data engineering experience building and operating large-scale, distributed data systems
Hands-on experience with modern data storage and processing technologies (e.g., Kafka, Postgres, blob storage, Flink, dbt, Iceberg)
Expertise in Python and SQL; experience with other languages like Java, Scala, or Go
Cloud-native experience in Azure, AWS, and/or Google Cloud, using container orchestration technologies like Docker, Kubernetes, and infrastructure-as-code tools like Terraform
Familiar with DevOps best practices, using version control (Git), CI/CD (e.g., Buildkite, GitHub Actions), and monitoring and alerting (e.g., Prometheus, Grafana, Datadog)
Experience with AI/ML, including LLMs and MCP servers
#LI-Onsite #LI-KL1
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