Overview

Senior Software Engineer, Data Platform

Responsibilities

• Design, build, test, deploy, and operate production-grade software applications and data products.
• Develop backend services, REST and GraphQL APIs, asynchronous workflows, web applications, and system integrations.
• Build event-driven solutions using messaging, streaming, queues, and event-bus technologies.
• Create reusable platform capabilities that allow product teams and data-domain owners to publish, discover, access, and manage trusted data products.
• Develop and maintain automated CI/CD pipelines, test automation, infrastructure configuration, and repeatable deployment processes.
• Design applications and services for reliability, scalability, performance, observability, and operational support.
• Implement logging, metrics, tracing, alerting, error handling, and production troubleshooting capabilities.
• Integrate applications with cloud services, enterprise platforms, third-party systems, identity providers, and internal APIs.
• Work with relational, NoSQL, analytical, streaming, and object-storage technologies as appropriate.
• Participate in on-call support and take ownership of the software and services the team delivers.

Qualifications

• Five or more years of professional software engineering experience building and operating production applications.
• Strong programming ability in at least one general-purpose language such as Python, Java, TypeScript/JavaScript, C#, Go, or Kotlin.
• Demonstrated experience building backend application services and well-designed APIs.
• Practical experience with event-driven architectures, message queues, streaming platforms, or event buses.
• Strong understanding of automated testing, including unit, integration, contract, and end-to-end testing.
• Hands-on experience creating or maintaining CI/CD pipelines and automated deployment processes.
• Solid knowledge of software architecture, design patterns, distributed systems, and production-operability concerns.
• Demonstrated ability to troubleshoot applications across code, infrastructure, networking, integration, and data boundaries.
• Ability to work across disciplines and take ownership of a capability from initial design through production support.
• Data and Analytics Qualifications
• Candidates should also have meaningful experience in one or more of the following areas:
• Data engineering, analytics engineering, or data-platform development
• Machine learning or applied data science
• Statistical analysis and experimentation
• Data pipelines, transformation frameworks, and orchestration
• Streaming data processing
• Data quality, lineage .