Build and operate modern, cloud-native enterprise applications that support critical Data Privacy initiatives as a Staff Privacy by Design engineer.
Compensation
The expected base compensation for this role is: $133,000 - $188,600. Actual base compensation within the identified range will vary based on factors relevant to the position.
Responsibilities
Design, develop, test, and maintain microservices and RESTful APIs using Java and Spring Boot on Microsoft Azure
Deliver assigned features and services from requirements clarification and implementation through testing, deployment, observability, and production support
Implement solutions within established architecture, patterns, and team standards; contribute to component-level designs and raise technical tradeoffs or concerns
Work with product, Legal, privacy partners, and teammates to translate Data Privacy and business requirements into clear implementation tasks
Build secure, reliable services using Azure capabilities, container-based workloads, and cloud-native patterns
Requirements
Bachelor’s degree in Computer Science, Software Engineering, or a closely related technical field, or equivalent demonstrable professional software engineering experience
Nice to have: Data Privacy background or experience
Nice to have: security best practices including OAuth2/OIDC, JWT, and secrets management
Nice to have: modernizing legacy or monolithic applications into microservices and cloud-native architectures
Lead the design and delivery of scalable, governed data products on a Databricks Lakehouse across Azure and AWS.
Compensation
Pay Range: $133,365.00 - $156,900.00
Responsibilities
Lead and guide Databricks data engineering across Bronze, Silver, and Gold layers, with particular focus on trusted, reusable Silver and Gold data products.
Translate business requirements into scalable data architecture, engineering designs, delivery standards, and technical acceptance criteria.
Remain hands‑on in engineering and development, including coding, code review, and code management, while guiding the delivery of batch, CDC, streaming, API, and file‑based data pipelines.
Review data models, solution designs, and critical code for quality, security, scalability, performance, and alignment with engineering standards.
Drive cross‑cloud data integration and sharing across Azure, AWS, Databricks, and Snowflake.
Senior Test Automation Engineer responsible for designing and building scalable automation frameworks for backend microservices across AWS and Azure ecosystems.
Responsibilities
Design, build, and maintain robust BDD/Data-Driven API test automation frameworks using Java, Rest Assured, TestNG/JUnit, and Maven/Gradle
Validate complex asynchronous backend middleware workflows, microservice contracts, JSON/XML payload schemas, and HTTP status codes
Execute automated test suites targeting RESTful endpoints deployed on cloud environments (AWS API Gateway, Lambda, Azure Functions, Azure App Services)
Automate security checks and token lifecycle validations using OAuth 2.0, JWT, and API Keys within Rest Assured requests
Integrate Rest Assured automation suites into automated build and deployment pipelines using Azure DevOps, Jenkins, or GitHub Actions
Lead and evolve the data foundation of Trane Technologies’ enterprise telematics SaaS platform, managing ingestion, transformation, storage, and analytics across Azure services.
Compensation
Annual Base Salary Range or Hourly Base Pay Range: $127,110.00 - $177,870.00
Responsibilities
Own end-to-end data architecture across Cosmos DB, Azure Data Explorer, Azure SQL, and Fabric
Design and optimize high-throughput ingestion pipelines for IoT devices
Build and maintain Azure Functions, Event Hubs, and Service Bus pipelines
Implement data quality, validation, and anomaly detection
Apply CI/CD practices for data pipeline deployment
Requirements
Bachelor’s degree in Computer Science, Mathematics, Information Technology, or a related field
Design, develop, and maintain scalable data pipelines, data products, and backend services supporting real-time decisioning, reporting, analytics, and AI use cases.
Responsibilities
Design, develop, and maintain scalable data pipelines, data products, and backend services supporting real-time decisioning, reporting, analytics, and AI use cases.
Design and implement data models for analytical and operational workloads, ensuring performance, scalability, and maintainability.
Produce high-quality, secure, testable, and maintainable code following engineering best practices.
Develop and maintain data governance, data quality, and monitoring solutions across the platform.
Collaborate with cloud, security, and architecture teams to ensure compliance with enterprise standards and cloud best practices.
Requirements
Bachelor’s or Master’s degree in Computer Science, Data Analytics, Information Systems, or a related technical field; or equivalent training and professional experience.
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