Senior Software Engineer - (Data Platform)

About the Role

**Position Summary** We are seeking a senior software engineer who combines strong, broad-based software engineering experience with practical knowledge of data platforms, analytics, and data science. This is a software engineering role first. The successful candidate must be able to design, build, deploy, operate, and support complete production applications—not only data pipelines, notebooks, database solutions, or isolated cloud services. They should be equally comfortable building an API, integrating enterprise systems, implementing an event-driven workflow, creating a web application, establishing a CI/CD pipeline, and helping operationalize a data or machine-learning capability. Experience with technologies such as AWS Glue, Apache Flink, Spark, data warehouses, and relational databases is valuable, but it is not sufficient on its own. We need an engineer who understands the full software development lifecycle and can turn data capabilities into reliable, secure, usable products. **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. Apply sound software engineering practices, including modular design, clean code, version control, code reviews, automated testing, dependency management, and secure development. 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. Help productionize data-science and machine-learning solutions by turning experimental code and models into secure, maintainable, observable services and workflows. Partner with data scientists, data engineers, architects, product managers, security teams, platform engineers, and application-development teams. Contribute to architecture decisions, technical standards, reusable patterns, and platform roadmaps. Mentor engineers whose experience has primarily focused on data tools, helping them adopt broader software engineering practices. Participate in on-call support and take ownership of the software and services the team delivers. **Required 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. Experience developing or contributing to web applications using modern frontend and backend frameworks. Experience with system integration patterns, authentication and authorization, API security, and service-to-service communication. 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. Experience deploying and operating applications in a public cloud environment, preferably AWS. Experience with containers, serverless technologies, or managed application-runtime platforms. Strong knowledge of software architecture, design patterns, distributed systems, and production-operability concerns. Experience with relational databases, data modeling, SQL, and at least one non-relational or analytical data technology. 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, governance, metadata, and discoverability Data lakes, lakehouses, warehouses, and modern analytical architectures Turning notebooks, models, or analytical prototypes into production software Familiarity with technologies such as Apache Flink, Spark, Kafka, AWS Glue, dbt, Iceberg, Snowflake, Databricks, Dremio, Athena, or similar platforms is beneficial. **Preferred Qualifications** Experience building internal developer platforms, self-service data platforms, or reusable enterprise capabilities. Experience applying data-mesh principles, including domain-owned data products, federated governance, and self-service platform enablement. Experience developing user-facing tools that make complex platform capabilities accessible to non-specialists. Experience with infrastructure as code using Terraform, AWS CDK, CloudFormation, or an equivalent technology. Experience with Kubernetes, ECS, Lambda, or other container and serverless deployment models. Experience implementing observability using tools such as OpenTelemetry, CloudWatch, Datadog, Grafana, or similar platforms. Experience integrating machine-learning models or generative-AI capabilities into production applications. Familiarity with MLOps, model deployment, model monitoring, feature management, and responsible AI practices. Experience working in a product-oriented engineering organization. **What Success Looks Like** A Successful Engineer In This Role Can Take a business or platform problem from concept through production without depending on another engineering team to provide basic application-development expertise. Choose appropriate technologies rather than treating every problem as a database, ETL, notebook, or streaming problem. Build secure and maintainable APIs, applications, integrations, and event-driven services around data capabilities. Establish automated testing, deployment, observability, and operational support as part of the solution—not as afterthoughts. Translate data-science prototypes into reliable production products. Collaborate effectively with both traditional software-engineering teams and specialized data professionals. Help the data platform operate as a product-oriented, cross-functional engineering organization.