VP, Principal Data Engineer

LPL Financial•Charlotte, NC

About The Position

LPL Financial is seeking a highly accomplished VP, Principal Data Engineer to modernize the investor experience across its Reporting, Statements and Letters platforms, which serve more than 2.4 million investors. The role provides strategic and hands-on technical leadership in designing, building, and modernizing enterprise-scale data platforms. The engineer will architect high-performance data pipelines, real-time streaming solutions, and cloud-native data services, while establishing standards, reusable frameworks, and practices that support business growth and operational excellence. Working with business leaders, architects, product teams, and engineering organizations, this leader will influence technical strategy, accelerate cloud transformation, and deliver data capabilities that enhance the investor experience. The role leads delivery and execution across engineering pods, mentors engineers, defines technical roadmaps and best practices, and ensures platform reliability, scalability, performance, and service-level objective compliance. It also drives operational excellence through data quality and observability capabilities, CI/CD and DevOps practices, production issue resolution, and cloud performance and cost optimization.

Requirements

10+ years of experience designing and developing large-scale ETL/ELT pipelines and data platforms using AWS, Glue, S3, RDS/Aurora, Snowflake, DBT, and Airflow.
8+ years of hands-on experience developing data solutions with Python.
6+ years of experience building event-driven and streaming architectures using Kafka, EventBridge, SQS, and related technologies.
6+ years of experience with relational and NoSQL databases, including performance tuning and optimization.
6+ years of experience with source control, CI/CD, and DevOps practices using GitHub, TeamCity, Jenkins, Octopus, or similar tools.
Technical leadership and architectural decision-making ability.
Deep expertise in data engineering, ETL/ELT, and cloud-native platforms.
Strong analytical, problem-solving, and strategic planning skills.
Ability to translate complex business requirements into scalable technical solutions.
Excellent communication and stakeholder management skills.
Proven ability to lead high-performing teams in fast-paced, agile environments.

Nice To Haves

5+ years of experience within financial services, wealth management, or brokerage organizations.
Experience with AI-assisted development tools such as GitHub Copilot, Copilot Chat, Cursor, or similar technologies.
Experience with Terraform, infrastructure automation, and data lake implementations.
AWS Associate-level certifications, such as AWS Certified Developer – Associate or AWS Certified Solutions Architect – Associate.
Experience leading enterprise cloud modernization and data transformation programs.
Experience developing APIs and microservices using .NET technologies.
Familiarity with GitHub Copilot and other AI-assisted development tools.

Responsibilities

Lead delivery of scalable, high-quality, cost-effective data solutions aligned with business priorities.
Own end-to-end execution across engineering pods, balancing employee and vendor resources to optimize delivery and mitigate operational risk.
Partner with business and technology leaders to prioritize initiatives, accelerate delivery, and improve engineering productivity.
Mentor engineers and promote engineering excellence, innovation, accountability, and continuous improvement.
Architect and build cloud-native data platforms for large-scale data ingestion, transformation, validation, and distribution.
Design, develop, and optimize ETL/ELT pipelines using Python, AWS, Snowflake, DBT, Airflow, and related technologies.
Develop event-driven and real-time data processing solutions using Kafka, EventBridge, SQS, SNS, and related technologies.
Modernize legacy platforms with secure, resilient, reusable cloud-native architectures and patterns.
Define technical roadmaps, engineering standards, and best practices that advance enterprise data capabilities.
Ensure platform reliability, scalability, performance, and service-level objective compliance.
Implement data quality, observability, auditing, reconciliation, and monitoring capabilities.
Drive CI/CD adoption, DevOps automation, and operational practices that improve resiliency and efficiency.
Lead root-cause analysis and resolution of complex production issues while optimizing cloud performance and cost management.
Partner with architects, product teams, and business stakeholders to deliver modern data products and platform capabilities.
Influence technical strategy, architecture decisions, automation initiatives, and continuous innovation across the data ecosystem.