Heading

Fort Mill, SC

Internship

Onsite

$25/hr - $30/hr

Intern

LPL Financial is a leading independent broker-dealer that provides technology, brokerage, and investment advisor services to financial advisors and institutions. The Data Engineering Intern will help build and optimize data platforms, pipelines, warehouses, and reporting solutions while supporting analytics, business intelligence, data products, and AI initiatives. The role also involves data validation, stakeholder collaboration, troubleshooting, automation, and documentation.

Apply Now

Responsibilities

Assist in building and maintaining data pipelines that ingest, process, and transform data from multiple sources
Support the development and optimization of data platforms, warehouses, lakes, and reporting solutions
Analyze, validate, and monitor data to ensure accuracy, quality, and reliability
Partner with data engineers, analysts, product managers, and business stakeholders to support analytics, reporting, and AI use cases
Contribute to the development of data products by gathering requirements, documenting business needs, and supporting product lifecycle activities
Leverage automation and AI-enabled tools to improve data workflows, operational efficiency, and insight generation
Assist with troubleshooting data pipeline issues, performance monitoring, and process optimization
Document data architectures, workflows, and technical specifications

Qualification

Required

Currently pursuing a bachelor's or master's degree in Computer Science, Management Information Systems (MIS), Data Science, Analytics, Engineering, Mathematics, or a related field
Available to work from of LPL Financials' primary office locations
Understanding of data pipelines, data modeling, data transformation, and data quality concepts
Ability to analyze data, identify trends, and support data-driven decision-making
Interest in translating business needs into data products and meaningful solutions
Curiosity about AI, Generative AI, machine learning, and how intelligent technologies

Preferred

Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP), including cloud-based data storage and processing services
Exposure to modern data engineering technologies, including Snowflake, Databricks, Redshift, BigQuery, Apache Spark, or Apache Kafka
Proficiency in SQL and familiarity with Python or other programming languages used to build, automate, and optimize data pipelines
Experience with ETL/ELT processes, data modeling, and workflow orchestration tools such as Airflow, dbt, or similar technologies
Familiarity with Git, Agile development practices, and data visualization tools such as Power BI, Tableau, or Looker