Job
Description
We are looking for 10 Software Engineer IIs (Data) to play a key role in delivering software products within a high-impact engineering team. You will collaborate with Product Managers, User Experience Designers, Architects, and other team members to modernize and build products aligned with the product teams vision and strategy. These products will leverage multi-cloud platforms, human-centered design, Agile, and DevOps principles to deliver industry-leading solutions at high velocity and exceptional quality. Youll be working alongside talented software engineering professionals, leading by example, mentoring others, and thriving in a fast-paced environment by embracing inclusive behaviors, demonstrating attention to detail, and navigating ambiguity. Team Overview The Data and AI enablement teams focus on accelerating end-to-end data and AI adoption across advanced analytics, product teams, and sales and operations functions. The broader team is responsible for data strategy, availability, and adoption via self-service tools, including AI model registry, activation of models for business, multichannel testing, and customer relationship measurement all in alignment with Responsible AI principles. The Sales Hub team supports seamless execution and distribution of sales transactions. It plays a pivotal role in delivering accurate data promptly, enabling better inventory management, financial planning, sales performance improvement, customer engagement, risk mitigation, and competitive advantage. Key Responsibilities Contribute to the delivery of complex solutions by breaking down big problems into smaller pieces Actively participate in team planning activities Ensure quality and integrity of the SDLC and identify opportunities to improve team practices through recommended tools and methods Triage complex issues independently Stay informed of the technology landscape and help plan delivery of broad business needs across multiple applications Set a consistent example of agile development practices and coach peers on cross-functional collaboration Mentor junior engineers and help new hires ramp up Contribute to and enhance internal libraries and tools Understand the business domain supported by your applications Proactively communicate status and issues to leadership Identify risks and challenges in your work and team deliverables Collaborate across teams to solve customer-centric problems creatively Show commitment to critical delivery deadlines Basic Qualifications 3+ years of relevant professional experience with a bachelors degree OR equivalent At least 1+ years in cloud engineering and architecture with AWS services (e.g., EC2, Lambda, S3, RDS, API Gateway, etc.) 2+ years of experience in microservices architecture using Java, Kafka, and NoSQL databases 2+ years of Java and Spring Boot development with a strong foundation in software engineering principles Experience building and deploying microservices Familiarity with CI/CD pipelines and tools such as Jenkins, GitLab CI, or AWS CodePipeline Ability to understand business problems and apply engineering design principles to solve for scalability, performance, and security Proven experience working directly with engineers, product managers, and stakeholders Strong communication and collaboration skills Preferred Qualifications Experience in omni-channel retail or sales environments Familiarity with Docker containerization and automated testing practices Passion for keeping up with new technologies and industry trends Proactive learning mindset and knowledge-sharing attitude Experience with monitoring/logging tools like Prometheus, Grafana, ELK stack, or AWS CloudWatch Understanding of security best practices in cloud environments Technical Skills (Tools, Technologies, Frameworks, Platforms) Programming Languages & Frameworks: Spring Boot Java Cloud Platforms & Services (AWS-focused): AWS EC2 AWS Lambda AWS S3 AWS RDS AWS API Gateway AWS CodePipeline AWS CloudWatch Microservices Architecture: Microservices Design & Development Service-to-service communication API design and implementation Databases: NoSQL Databases Data Streaming / Messaging Systems: Kafka DevOps & CI/CD Tools: Jenkins GitLab CI CI/CD Pipelines (general understanding and implementation) Containerization & Virtualization: Docker Monitoring and Logging Tools: Prometheus Grafana ELK Stack (Elasticsearch, Logstash, Kibana) AWS CloudWatch (mentioned above under AWS) Applied Technical Skills (Practices, Design Principles, Methodologies, etc.) Software Engineering Principles: Modular and scalable architecture Object-oriented programming (OOP) Code quality, maintainability, and reusability best practices Agile Development Methodologies: Scrum/Kanban practices Agile ceremonies (Planning, Standups, Retrospectives) DevOps Practices: Continuous Integration / Continuous Deployment Infrastructure as Code mindset System Design and Architecture: Breaking down complex systems into smaller manageable components Designing solutions that scale and perform under load Cloud Engineering & Architecture Principles: Multi-cloud awareness Designing for resilience, fault-tolerance, and cost-efficiency AI/ML Enablement (Supporting Systems, not core model development): AI model registry integration Model activation pipelines Responsible AI principles (adoption, governance) Data Engineering & Analytics Support: Data availability and strategy design Self-service data tooling enablement Multichannel testing frameworks Customer relationship measurement systems Collaboration & Communication: Working with cross-functional teams (Product, UX, Architecture) Problem-solving with cross-team dependencies Proactive communication and status reporting Quality Engineering: Automated Testing practices Ensuring high-quality software delivery through unit testing, integration testing, and regression testing Security Best Practices (Cloud-focused): Secure deployment patterns Identity and access management in cloud setups
