Designs, implements, and deploys secure, resilient, highly scalable, fault-tolerant services that integrate with enterprise systems; ensures functional, performance, scalability, security, governance, and auditability requirements are met
Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
Applies backend infrastructure patterns (e.g., load balancing, autoscaling) and implements monitoring/alerting for performance, scalability, availability, and reliability
Drives consistent validation standards (secure coding, peer review, automated testing) and promotes reuse of effective patterns across the team
Leads and mentors junior team members in a high-pressure delivery environment; manages multiple deliverables across business groups and strengthens stakeholder relationships
Defines and implements guardrails and evaluation approaches for agentic AI used in production applications to deliver business value while meeting quality, safety, latency, and cost requirements
Collaborates with LOB users, SMEs, architects, DBAs, and system administrators to design solutions, manage enhancements, and resolve issues
Required qualifications, capabilities, and skills
Formal training or certification in computer science or software engineering concepts and 5+ years of applied experience
10+ years of hands-on software development experience in large-scale distributed systems, primarily in Java, modern Java/Spring Boot microservices, and Python
Hands-on practical experience in system design, application development, testing, and operational stability
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices
Strong experience with REST APIs and service-oriented / microservices architecture
Strong experience with Spark and SQL; big data processing with focus on performance and optimization
Kubernetes orchestration experience (building, deploying, and operating production services)
Experience with AWS services and tooling (e.g., Terraform, EMR, EKS/ECS, Lambda, RDS, S3)
Experience with log analytics / observability tools (e.g., Splunk, AWS CloudWatch, Datadog)
Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Preferred qualifications, capabilities, and skills
Contributes to software engineering communities of practice and events that explore new and emerging technologies
Adds to team culture of diversity, opportunity, inclusion, and respect
Excellent oral and written communication, and problem-solving skills
Experience with Scala programming language
Experience with or exposure to Snowflake or Databricks cloud platforms
Experience with Open Table Format like Iceberg (preferred)
