New Grad - 2027 - Data Engineering

Data Engineering New Grad 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.

• 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.

What are we looking for?

We're looking for strong collaborators who deliver exceptional client experiences and thrive in fast-paced, team-oriented environments. Our ideal candidates pursue greatness, act with integrity, and are driven to help our clients succeed. We value those who embrace creativity, continuous improvement, and contribute to a culture where we win together and create and share joy in our work.

• 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 Financial's primary office locations

• Data Engineering Fundamentals – Understanding of data pipelines, data modeling, data transformation, and data quality concepts.
• Analytics & Business Insight – Ability to analyze data, identify trends, and support data-driven decision-making.
• Product & Stakeholder Mindset – Interest in translating business needs into data products and meaningful solutions.
• AI & Innovation – Curiosity about AI, Generative AI, machine learning, and how intelligent technologies

• 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, Big Query, 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.

Priority Application Date:
September 21 at 11:59 PM PST

Disclaimer for international students:

• At this time, for our early career program positions, we're unable to consider candidates who require sponsorship now or in the future. For other positions, within LPL it will depend on the specific position.
• You will be responsible for obtaining and providing to LPL the required I-9 documentation as part of our onboarding process.
• Positions offered are for full-time work at 40 hours per week.
• Please consult your Designated School Official to confirm your eligibility with your school ability prior to applying.

Pay Range: $30.05-$50.05/hour Actual base salary varies based on factors, including but not limited to, relevant skill, prior experience, education, base salary of internal peers, demonstrated performance, and…