Engineer II, Data (Cloud & AI)

About the role

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.

Responsibilities

Data Engineering

• 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

Cloud Engineering

• 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

AI-Assisted Engineering

• 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

Production Support & Reliability

• Participate in application support and incident response processes
• Troubleshoot and resolve production pipeline failures
• Conduct root cause analysis and drive preventative improvements
• Support platform monitoring and operational reporting
• Contribute to runbooks and operational documentation

Collaboration

• Participate in Agile ceremonies and sprint activities
• Work closely with product managers, architects, analysts, and business stakeholders
• Collaborate with vendor teams and upstream/downstream data consumers
• Contribute to architecture discussions and technical design reviews

Key Objectives

• Deliver scalable and resilient data ingestion solutions
• Improve AWS cloud infrastructure and operational maturity
• Implement AI-enabled engineering solutions where appropriate
• Reduce manual support effort through automation
• Maintain high platform availability and service quality
• Support enterprise data governance and security standards

Requirements

• Bachelor’s degree in Computer Science, Engineering or related field with minimum of 3 years of experience in software engineering, cloud engineering, platform engineering, or data engineering OR Master's degree in Computer Science, Data Engineering, Artificial Intelligence, Information Systems, Engineering, or a related field with minimum of 1 year of relevant experience
• Demonstrated experience building, supporting, or maintaining cloud-based applications, data platforms, or production systems
• Hands-on experience with AWS services including S3, Lambda, Glue, CloudWatch, IAM, EventBridge, and Athena
• Proficiency in Python and/or PySpark for data transformation, SQL, API Integrations, and Git/GitHub
• Experience in data engineering to include ETL/ELT pipeline development, Data validation frameworks, Data quality practices, and Batch and event-driven processing

Core Competencies

• Strong analytical, troubleshooting, and problem-solving skills
• Excellent debugging and troubleshooting capabilities
• Effective communication and collaboration
• Ability to work independently and deliver in fast-paced environments
• High attention to detail and commitment to data quality
• Ability to work independently while collaborating effectively within Agile teams
• Continuous learning mindset, including adoption of AI-assisted engineering practices in a responsible manner

Preferences

• AWS certification(s)
• Financial services experience
• Data platform engineering
• Event-driven architectures
• Large-scale file processing
• Production support and on-call responsibilities
• Observability and monitoring platforms
• Terraform, Infrastructure as Code, CI/CD pipelines
• Experience building solutions using:
  • Amazon Bedrock
  • Azure OpenAI
  • OpenAI APIs
  • Vector databases
  • Retrieval Augmented Generation (RAG)
• Understanding of:
  • Prompt engineering
  • LLM evaluation
  • AI governance and security
  • Agentic workflows
  • AI-powered automation

Pay

$40.10–$66.83/hour. Actual base salary varies based on factors, including but not limited to, relevant skill, prior experience, education, base salary of internal peers, demonstrated performance, and geographic location.

Additionally, LPL Total Rewards package is highly competitive, designed to support your success at work, at home, and at play – such as 401K matching, health benefits, employee stock options, paid time off, volunteer time off, and more. Your recruiter will be happy to discuss all that LPL has to offer!