VPII, Data Engineering Manager

LPL Financial•Charlotte, TX

2d•$169,126 - $281,808

About The Position

The VPII, Data Engineering Manager will lead a team and provide day-to-day technical and delivery leadership to advance complex programs. The role oversees LPL's enterprise data orchestration capability, data observability, and modernization of legacy batch workloads onto the Data Foundations Platform. Success requires deep data engineering experience, strong AWS and orchestration expertise, the ability to navigate legacy environments, and a record of building teams that operate with increasing autonomy. The manager owns engineering roadmaps and the end-to-end design, delivery, testing, deployment, reliability, and production support of large-scale data pipelines, databases, platforms, and event-driven workflows. They guide architecture and engineering decisions, establish standards for quality and operations, partner with cross-functional teams to deliver commitments, develop team capability, and evaluate emerging data technologies.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related quantitative discipline, or equivalent professional experience.
10+ years of progressive data engineering or data-platform experience, including at least 5 years leading technical teams and delivery in complex enterprise environments.
Demonstrated experience designing and modernizing large-scale data pipelines, databases, and data platforms that process high-volume, business-critical workloads.
Production experience delivering end-to-end workflows in AWS, including infrastructure as code with Terraform and orchestration using Airflow or comparable technologies.
Experience leading in modern and legacy data environments, including SQL, relational and NoSQL databases, ETL and batch processing, event-driven architectures, data modeling, lineage, and production support.
Deep data engineering and platform architecture expertise.
Technical leadership, sound judgment, and decisive execution.
Large-scale team leadership, coaching, and talent development.
Roadmap ownership, prioritization, and executive communication.
Production reliability, observability, and operational accountability.

Nice To Haves

Experience with AWS services such as Glue, Lambda, EC2, RDS, DynamoDB, S3, and API Gateway, as well as Confluent Kafka and Snowflake.
Proficiency in Python, Java, SQL, or .NET, with the ability to participate credibly in architecture discussions, technical designs, and code reviews.
Experience applying domain-driven design and delivering capabilities across backend, middleware, and front-end technologies.
Financial-services or other highly regulated industry experience with complex data, control, resiliency, and audit requirements.
Experience modernizing environments with approximately large ETLs and workloads ranging from hundreds of thousands to tens of millions of records per job.

Responsibilities

Set and execute the engineering roadmap for enterprise data orchestration, data observability, batch modernization, and migration of legacy workloads to the Data Foundations Platform.
Direct end-to-end design, delivery, testing, deployment, reliability, and production support of large-scale data pipelines, databases, platforms, and event-driven workflows.
Guide architecture decisions, review engineering trade-offs, unblock teams, and ensure solutions are secure, reliable, scalable, and supportable.
Modernize legacy patterns across Informatica, SQL Server and stored procedures, .NET, and proprietary technologies while reducing dependency on legacy capabilities, including DIS and LPL SQL.
Establish engineering standards for data quality, lineage, observability, infrastructure as code, testing, deployment, resiliency, and operational support.
Partner with Product, Architecture, SRE, Quality Engineering, Scrum, and business teams to sequence work, manage dependencies, communicate trade-offs, and deliver roadmap commitments.
Build team capability through hiring, coaching, succession planning, vendor management, and development of junior engineering talent; establish an operating model in which teams increasingly self-organize.
Evaluate emerging data technologies and recommend adoption based on business value, architectural fit, operational risk, and total cost of ownership.