Resilience, Testability & Scalability Lead

Role Overview:
The ideal candidate will have a deep background in designing highly available systems, implementing robust disaster recovery, managing scalable cloud infrastructure, and building automated, testable, and observable platforms-especially within AWS and Kubernetes environments.

Key Responsibilities:
Design and implement high availability and failover strategies across multi-zone AWS deployments
Lead the development and execution of disaster recovery and business continuity plans, including RTO/RPO validation and cross-region strategies
Define testability strategies, test data management frameworks, and performance testing protocols
Enable infrastructure and application resilience by introducing circuit breakers, retry patterns, service meshes, and graceful degradation mechanisms
Establish real-time monitoring, alerting, and log aggregation frameworks using tools like CloudWatch and Prometheus
Drive test automation and quality engineering best practices, integrating with CI/CD pipelines
Optimize application and data layer performance through query tuning, caching, and indexing strategies
Scale data processing using distributed frameworks like Apache Spark, and implement event-driven stream processing with Kafka
Collaborate with platform, DevOps, and SRE teams to ensure resource efficiency, cost control, and performance SLAs
Contribute to regulatory readiness by enforcing security, encryption, and audit logging standards

Required Skills & Experience:
Infrastructure Resilience & DR:
Multi-AZ deployments, auto-scaling, load balancing, circuit breakers
Disaster recovery design: backup/restore, cross-region replication, RTO/RPO

Monitoring & Observability:
Experience with CloudWatch, Prometheus, log aggregators
Set up alerting for incident response, latency, throughput, and error rates

Application Resilience & Security:
Error handling, service degradation, exponential backoff
Security best practices: IAM policies, encryption at rest/transit
Familiarity with FINRA/SIPC compliance standards (preferred)

Test Automation & Quality:
Unit testing (e.g., pytest), integration testing, E2E automation
Test data generation, synthetic data, environment provisioning
Performance testing using JMeter, Gatling, stress and capacity testing
Code reviews, static analysis, data validation, anomaly detection

Scalability & Optimization:
Horizontal scaling using Kubernetes, Docker, service discovery
API Gateway, caching layers (Redis, Memcached), DB partitioning
Connection pooling, capacity planning, cost-aware architecture

Data & Stream Processing:
Spark cluster management, parallel processing, big data optimization
Kafka-based messaging, windowing, and aggregation for real-time data

Preferred Qualifications:
Experience in financial services or regulated environments
Familiarity with LPL's enterprise data and platform modernization initiatives
AWS or Kubernetes certifications
Strong communication skills and cross-functional collaboration experience