Engineer II, Data (Cloud & AI)

LPL Financial•Charlotte, TX

About The Position

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.

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
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

Nice To Haves

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, Infrastrucutre 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

Responsibilities

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.
Support platform monitoring and operational reporting.
Contribute to runbooks and operational documentation.
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.