Engineer II, Data Cloud & AI - lplfinancial

Where Ambition Meets Innovation

Build a career that matches all your initiative with an impressive dose of innovation. From cutting-edge resources and a collaborative environment to the freedom to make an impact and more, you’ll find the ingredients you need at LPL Financial to shape your success while helping clients pursue their financial goals.

LPL Financial is looking for an Engineer II, Data who can build and operate cloud-native data solutions at enterprise scale. This role combines AWS cloud engineering, data pipeline development, platform reliability, and AI-assisted software development. The ideal candidate enjoys solving operational challenges, automating manual processes, and using modern AI tools to accelerate engineering outcomes while supporting mission-critical production systems.

The Engineer II, Data (Cloud & AI) is responsible for designing, building, supporting, and optimizing cloud-native data solutions within the Enterprise Data Integration Framework (EDIF).

This role supports the ingestion, validation, transformation, enrichment, and standardization of enterprise data while leveraging modern cloud and AI technologies to improve engineering productivity, operational efficiency, and platform observability.

The ideal candidate combines strong data engineering fundamentals with AWS cloud experience and practical experience using AI-assisted development tools and generative AI technologies.

• Design, develop, and maintain cloud-based data ingestion and transformation pipelines.
• Support onboarding of new vendor and enterprise data sources.
• Optimize processing performance for large-volume datasets.
• Build reusable ingestion, validation, and transformation frameworks.
• Develop automated data quality validation processes.

• Develop and support AWS-based solutions.
• Build infrastructure using Terraform and Infrastructure as Code practices.
• Support CI/CD deployment pipelines.
• Improve platform scalability, resiliency, and disaster recovery readiness.
• Implement monitoring and observability capabilities.

• Utilize AI coding assistants to improve development velocity and engineering efficiency.
• Develop proof-of-concept solutions leveraging LLMs and generative AI services.
• Build intelligent operational tooling for monitoring, troubleshooting, and support workflows.
• Identify opportunities where AI can reduce engineering effort or improve service delivery.
• Evaluate and implement AI‑driven automation capabilities within established governance standards.

• Participate in application support and incident response processes.
• Troubleshoot and resolve production pipeline failures.
• Conduct root cause analysis and drive preventative improvements.