New Grad - 2027 - Data Engineering

LPL Financial•Fort Mill, SC

1d•$30 - $50•Onsite

About The Position

LPL Financial is seeking a motivated Data Engineering new grad to help build and optimize the data platforms that power analytics, business intelligence, and AI-driven decision-making. In this role you'll work with data engineering, analytics, and data product teams to develop data pipelines, support reporting and insights, and contribute to AI and Generative AI initiatives. This role is ideal for recent graduates who are passionate about data, technology, and using intelligent solutions to solve business challenges and drive client value.

Requirements

Completed 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

Nice To Haves

Previous internship experience within a large-scale enterprise environment
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.

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.