VP, Principal Data Engineer

LPL Financial•Fort Mill, SC

8d•$159,959 - $266,564

About The Position

LPL Financial is seeking a highly accomplished VP, Principal Data Engineer to lead modernization of the investor experience across its Reporting, Statements and Letters platforms, serving more than 2.4 million investors. The position provides strategic and hands-on technical leadership in designing, building, and modernizing enterprise-scale data platforms. It architects high-performance data pipelines, real-time streaming solutions, and cloud-native data services, while establishing engineering standards, reusable frameworks, and best practices that support business growth and operational excellence. The role works closely with business leaders, architects, product teams, and engineering organizations to influence technical strategy, accelerate cloud transformation, and deliver data capabilities that enhance the investor experience. The successful candidate leads scalable, high-quality, cost-effective data delivery and end-to-end execution across engineering pods, balancing employee and vendor resources. The role also mentors engineers, advances engineering practices and platform reliability, and leads resolution of complex production issues. It requires extensive experience with AWS, Python, Snowflake, DBT, Airflow, event-driven systems, databases, CI/CD, and DevOps. The salary range is $159,959 to $266,564; actual base salary varies based on relevant factors. LPL also describes its Total Rewards package as including 401K matching, health benefits, employee stock options, paid time off, and volunteer time off.

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 capabilities.
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 the delivery of scalable, high-quality, and cost-effective data solutions aligned to 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 through 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 best practices to 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.