Job Description & Details
This is a heavy-duty data engineering gig with JPMC in Plano, requiring you to be local for an in-person second round. You will be building massive ETL and streaming pipelines, so expect to spend your days knee-deep in PySpark, Java, and AWS infrastructure.
What You'll Actually Be Doing
You will be designing, building, and scaling data processing solutions that handle heavy workloads using PySpark and Apache Spark. Expect to wire up various AWS services like SQS, Lambda, S3, and RDS while dealing with the realities of application resiliency and security. Because this is a mature enterprise environment, you'll also be collaborating closely with cross-functional teams in an Agile setup, writing clean code, and occasionally touching Terraform for infrastructure automation or exploring practical LLM use cases with Azure OpenAI.
The Core Tech Stack
You need absolute fluency in PySpark, Java, Spark, and Python to survive here. The data pipelines you build will rely heavily on AWS cloud services like SQS, Lambda, S3, and RDS, so shallow knowledge won't cut it. Experience with CI/CD, Test-Driven Development, and Unix-based shell scripting will be your daily bread and butter to keep these enterprise pipelines running smoothly.
Interview Expectations
Be prepared for a deep dive into Spark internals, particularly how you optimize shuffles, manage memory, and handle data skew in large PySpark jobs. The interviewer will also likely grill you on your AWS architecture choices, asking you to defend how you tie together SQS, Lambda, and S3 for fault-tolerant event-driven data ingestion. They want to see practical engineering tradeoffs, not just textbook definitions.
Application Advice
Make sure your resume explicitly highlights your hands-on production experience with PySpark, Java, Spark, and AWS (SQS, Lambda, S3, RDS) right at the top so the ATS picks it up instantly. Since this role requires local candidates in Plano, TX for an in-person interview, make your current location unmistakable on your resume to avoid getting auto-rejected by a recruiter screening for location.