Lead Software Engineer

Our client is currently seeking a Lead Software Engineer


This role requires a strong combination of software engineering, AWS data platform development , Integration architecture , DevOps automation , and Technical leadership . The successful candidate will establish development standards, guide other engineers, conduct code and design reviews, and translate architectural direction into production-ready solutions.


PRIMARY RESPONSIBILITIES

• Lead the design and development of an AWS-based operational data layer containing replicated data from mainframe policy administration systems.
• Build ingestion and processing components supporting cross-platform data ingestion and integration, batch integration, and event-driven data movement.
• Develop data transformation, mapping, validation, reconciliation, and exception-handling capabilities.
• Implement solutions using appropriate AWS services such as Amazon S3, Aurora or RDS, DynamoDB, AWS Glue, Lambda, and managed messaging services.
• Establish controls for data completeness, consistency, lineage, security, retention, and auditability.
• Implement monitoring for data latency, throughput, data-quality exceptions, failed transactions, and platform availability.
• Partner with mainframe, data engineering, and integration teams to resolve source-data and data movement issues.

• Design and build orchestration services that respond to business events and coordinate processing across legacy and modern platforms.
• Develop APIs, services, workflows, routing rules, transformation logic, and reusable integration components.
• Implement reliable processing patterns including idempotency, retries, replay, sequencing, error recovery, and dead-letter handling.
• Support both synchronous and asynchronous integrations using REST APIs, queues, topics, and event-driven patterns.
• Build configurable business rules and exception-management capabilities that reduce hard-coded processing.
• Ensure orchestration services provide traceability from the originating event through downstream processing.
• Collaborate with Product Owners, architects, analysts, QA engineers, and application teams to translate business processes into implementable technical designs.

• Establish and maintain automated CI/CD pipelines for application, data, and infrastructure deployments.
• Develop Infrastructure as Code using approved tools such as Terraform, AWS CloudFormation, or AWS CDK.
• Help configure application-specific AWS resources within enterprise cloud architecture and security guardrails.
• Implement automated build, test, security-scanning, deployment, rollback, and environment-promotion processes.
• Support AWS networking, IAM roles and policies, secrets management, encryption, logging, and environment configuration in partnership with Cloud Platform and Security teams.
• Implement operational dashboards, alerts, distributed tracing, and production-support procedures.
• Improve platform reliability, scalability, performance, recoverability, and cost efficiency.
• Create technical runbooks and support the transition of solutions into production operations.

• Serve as the hands-on technical lead for developers assigned to the Transformation team.
• Break architectural designs and product features into implementable engineering components.
• Establish coding, integration, testing, documentation, and deployment standards.
• Lead technical design sessions, code reviews, troubleshooting, and root-cause analysis.
• Mentor developers in AWS engineering, event-driven architecture, data integration, DevOps, and modern software-development practices.
• Delegate technical work while maintaining accountability for solution quality and integration.
• Identify technical dependencies, delivery risks, capacity constraints, and architectural decisions requiring escalation.
• Develop reusable frameworks and patterns that can be adopted across multiple transformation initiatives.
• Promote automated testing, secure coding, observability, and engineering discipline throughout the development lifecycle.

• Eight or more years of professional software-engineering experience, including responsibility for complex enterprise applications or platforms.
• Demonstrated experience serving as a technical lead, lead developer, or senior engineer responsible for guiding other developers.
• At least four years of hands-on AWS development or cloud-platform engineering experience.
• Strong programming experience in Java, Python, or another enterprise development language.
• Experience building APIs, microservices, data-processing services, and event-driven applications.
• Experience with AWS data and integration services, relational databases, NoSQL databases, and object storage.
• Experience designing data-ingestion, replication, transformation, reconciliation, or operational data-processing solutions.
• Experience building CI/CD pipelines and deploying Infrastructure as Code.
• Working knowledge of cloud security, IAM, encryption, secrets management, networking, logging, and monitoring.
• Experience with automated unit, integration, performance, and deployment testing.
• Strong troubleshooting skills across applications, data pipelines, cloud services, and infrastructure.
• Ability to communicate technical decisions, tradeoffs, risks, and dependencies to both technical and nontechnical stakeholders.

• AWS Developer, Solutions Architect, DevOps Engineer, or Data Engineer certification.
• Experience integrating AWS applications with mainframe platforms.
• Familiarity with COBOL, DB2 for z/OS, VSAM, CICS, batch processing, or mainframe data structures.
• Experience with cross-platform data integration tools and technologies.
• Experience with Amazon EventBridge, SQS, SNS, MSK or Kafka, Step Functions, ECS, EKS, or Lambda.
• Experience building high-volume, highly available, and auditable financial-services platforms.
• Familiarity with insurance, annuity, policy administration, or regulated financial-services environments.
• Experience with Snowflake or enterprise analytical data platforms while maintaining separation between operational and analytical workloads.
• Experience with legacy-system modernization patterns, including strangler architecture, parallel processing, reconciliation, and phased migration.
• Experience using AI-assisted engineering tools for code analysis, development, testing, or technical documentation.