Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Drive engineering excellence by producing high-quality production code, performing rigorous code reviews, improving test coverage, and ensuring adherence to secure coding and organizational engineering standards; Design and implement resilient distributed systems that meet performance, scalability, availability, and maintainability requirements.
Develop and deploy cloud-native services on AWS, leveraging services such as ECS, containerization with Docker, and automated CI/CD delivery practices.
Design, optimize, and maintain data models, schemas, and queries in PostgreSQL, ensuring data integrity, performance, and scalability across distributed services.
Produce and review architecture, technical design, and implementation artifacts for complex applications, ensuring alignment with enterprise architecture, security, and operational standards; Identify recurring technical or operational issues and lead automation, remediation, and resiliency improvements to reduce toil and improve application stability.
Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, testing effectiveness, documentation, troubleshooting, and operational outcomes.
Establish team standards for validating AI-generated outputs, including secure coding review, correctness checks, performance validation, automated testing, and responsible handling of data; Lead technical evaluation sessions with internal stakeholders, vendors, and external partners to assess architecture designs, integration approaches, technical feasibility, and alignment with existing systems.
Analyze application telemetry, operational data, quality metrics, and delivery patterns to identify trends and drive continuous improvement in engineering practices and system reliability; Contribute to engineering communities of practice and support initiatives focused on modern software engineering, cloud-native development, automation, AI-assisted delivery, and emerging technologies.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Strong hands-on experience designing, developing, and maintaining enterprise-grade applications using Java and Spring Boot.
Demonstrated experience building and supporting RESTful APIs, microservices, and distributed service-oriented systems; Practical cloud-native development experience, preferably with AWS, including ECS, containers, and cloud deployment patterns.
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Hands-on experience with Docker, containerized applications, and modern deployment practice; Strong understanding of CI/CD pipelines, automated testing frameworks, release automation, and DevOps engineering practices.
Solid experience with PostgreSQL, including relational data modeling, query optimization, indexing strategies, and performance tuning; Advanced understanding of the software development lifecycle, agile methodologies, application resiliency, observability, and secure software delivery.
Proven ability to lead technical design discussions, evaluate implementation options, and make sound engineering decisions for complex systems; Experience reviewing code, mentoring engineers, and promoting engineering best practices across a team.
Demonstrated experience adopting or leading effective use of approved AI-assisted software development tools for coding, code review, test acceleration, documentation, refactoring, or troubleshooting.
Strong understanding of responsible AI usage in engineering workflows, including validation of AI outputs, data sensitivity considerations, secure handling of inputs and outputs, and adherence to security and resiliency expectations.
Strong analytical, debugging, and problem-solving skills with the ability to diagnose complex technical issues across applications, infrastructure, and data layers; Excellent communication and collaboration skills, with the ability to influence technical direction and partner effectively with cross-functional stakeholders.
Preferred qualifications, capabilities, and skills
Experience with synchronous REST integration patterns for latency-sensitive credit/bureau calls, plus event-driven and asynchronous processing using Kafka, Amazon SQS, or similar — including dual-persistence patterns (e.g., Aurora PostgreSQL for transactional data and Snowflake for analytics and long-term audit).
Familiarity with API gateway technologies, service mesh concepts, and IDAnywhere / ADFS OAuth2 authentication/authorization patterns, plus edge-proxy vendor integration patterns (ELB → ECS → Secrets Manager → edge proxy → vendor API) and API lifecycle management for third-party bureau integrations.
Experience with infrastructure-as-code tools such as Terraform (Atlas / TFE), , or similar automation frameworks, and CI/CD delivery with blue/green deployments; Hands-on experience with observability tools, including logging, metrics, tracing, dashboards, and alerting platforms (Splunk, Dynatrace, CloudWatch, Grafana), and driving production readiness for regulated, high-availability services.
Experience designing systems for high availability, fault tolerance, scalability, and operational resilience — including ECS NextGen cluster migrations, multi-AZ resiliency, and graceful degradation for downstream vendor outages; Familiarity with performance testing, load testing, and application tuning (BlazeMeter / JMeter) for high-throughput, low-latency distributed systems under real-time credit-decisioning SLAs.
Experience with modern security practices, including secrets management (AWS Secrets Manager, Hera KMS, AppProtect), vulnerability remediation , dependency management, secure API design, and controls for 15-year audit retention and consumer credit data (soft-pull vs hard-pull semantics); Experience leading technical modernization, platform migration, and cloud adoption— and application resiliency initiatives across a regulated environment; Track record of contributing to engineering communities of practice, reusable frameworks, and technical standards , and mentoring engineers toward consistent, secure, and scalable delivery.
