Senior Quality Engineer – Enterprise Data Integration Framework (EDIF)

LPL Financial•San Diego, CA

1d•Hybrid

About The Position

As a Senior Quality Engineer for the Enterprise Data Integration Framework (EDIF) at LPL Financial, you will play a critical role in ensuring the quality, reliability, and performance of our foundational data integration solutions. You will be responsible for designing, developing, and executing comprehensive test strategies for large-scale data pipelines and integration services, contributing to the integrity of LPL's core data assets.

Requirements

Bachelor’s degree in Computer Science, Information Technology, Engineering, Information Systems, or a related discipline.
5+ years of experience in Quality Engineering, Data Quality, or Test Automation, with a strong focus on data-centric applications and data integration platforms.
5+ years experience validating data pipelines, ETL/ELT workflows, and distributed data systems, preferably within enterprise-scale data integration frameworks.
5+ years experience in SQL for complex data querying, reconciliation, and validation.
5+ years experience in Python for test automation, data validation, and scripting.
3+ years experience with AWS cloud services, including S3, Lambda, Glue, Step Functions, CloudWatch, IAM, and Athena; experience with other cloud platforms (Azure, GCP) is a plus.
Strong communication skills with the ability to explain complex technical issues to both technical and non-technical stakeholders.
Ability to work independently while thriving in a highly collaborative, fast-paced environment.

Nice To Haves

Strong understanding of data warehousing concepts, data modeling, and relational database management systems.
Solid knowledge of data quality and validation concepts, including schema evolution, referential integrity, reconciliation, negative testing, and edge-case handling.
Experience using Git-based version control and integrating tests into CI/CD pipelines.
Familiarity with Agile/Scrum development methodologies and working within iterative delivery models.
Excellent analytical, debugging, and problem-solving skills, with strong attention to detail.
Financial services industry experience is highly desirable.

Responsibilities

Design, develop, and implement robust test plans, test cases, and test scripts for complex EDIF data integration and ingestion workflows.
Perform functional, integration, system, regression, performance, and data validation testing across diverse data sources and targets.
Build and maintain automated test suites for AWS-based data pipelines using Python, PySpark, SQL, and AWS test harnesses.
Validate end-to-end ingestion workflows across Amazon S3, AWS Lambda, AWS Glue, Step Functions, EventBridge, and downstream data stores.
Verify schema evolution, data transformations, and enrichment logic applied in Glue ETL jobs.
Create and maintain reusable test datasets, mock files, and validation utilities supporting EDIF microbatch processing.
Perform in-depth data analysis and reconciliation to ensure data accuracy, completeness, and consistency throughout the EDIF platform.
Validate and monitor EDIF’s event-driven observability, including event lifecycle logging, CloudWatch metrics, error events, alerts, and recovery workflows.
Identify, document, prioritize, and track defects; partner with development teams for timely resolution and retesting.
Work closely with data engineers, developers, and architects to understand data models, data flows, and business requirements to ensure comprehensive test coverage.
Partner with Data Engineers to ensure code is testable, resilient, and aligned with QA automation best practices.
Participate in code reviews and design discussions, providing input on testability, quality, and potential risks.
Participate in release management, change control, and pre-deployment quality gates to ensure readiness across all ingestion pipelines.
Support nightly ingestion cycles by maintaining test coverage for new vendors, schema changes, schedules, and data-quality requirements.
Contribute to the ongoing enhancement of QA processes, methodologies, automation maturity, data quality controls, and QA standards within the team.
Mentor junior quality engineers and share best practices in data quality assurance.
Stay current with industry best practices in data quality, data governance, and data integration technologies.