Job Description & Details
This is a hands-on data engineering gig focused on wrangling massive datasets and building out rock-solid data pipelines for an organization currently anchored in Irvine before a 2027 move to San Dimas. You'll spend your days untangling legacy data structures and making sure downstream analytical models actually have clean, reliable data to work with. If you enjoy deep database work and scaling out ETL pipelines without getting bogged down by endless meetings, this role gives you the technical runway to just build things right.
What You'll Actually Be Doing
Expect to spend a good chunk of your week designing, writing, and optimizing heavy data pipelines that pull from a messy mix of APIs, relational databases, and flat files. You'll be untangling data integration bottlenecks, figuring out why certain enterprise databases are throwing weird discrepancies, and working closely with warehousing structures to make analytics and reporting run smoothly. The day-to-day is a mix of heavy SQL development, python scripting, and keeping an eye on data reliability across multiple enterprise source systems.
The Core Tech Stack
You are going to live and breathe Advanced SQL, PL/SQL, and Python, so you better be comfortable writing complex queries and automation scripts off the top of your head. They also need someone who really understands enterprise relational database platforms and distributed processing tools like Spark, especially since you'll be handling large-scale data sets and building out dimensional models. Knowing your way around modern ETL/ELT best practices and data warehousing fundamentals is non-negotiable here if you want to keep up with the data volume.
Interview Expectations
Be ready for the hiring manager to throw a complex query optimization problem at you, asking you to refactor a poorly performing PL/SQL script or dissect why a multi-source ETL pipeline is bottlenecking. They aren't just looking for syntax; they want to see how you troubleshoot data discrepancies and whether you can reason through edge cases in distributed processing. They are secretly testing your architectural instincts to see if you build data models that scale or if you just slap temporary fixes on structural problems.
Application Advice
When you tweak your resume for this one, make sure your SQL, Python, and ETL architecture experience are front and center right at the top of your employment history. Drop specific keywords like 'dimensional modeling', 'data warehousing', and 'PL/SQL optimization' directly into your bullet points so you clear the initial ATS filters without issue. Emphasize scale—mention the volume of data you've handled and the complexity of the source systems you've integrated, as hiring managers here want proof you've tackled messy enterprise data before.