Job Description & Details
This is a 6-month contract role based out of McLean, VA, requiring you to be onsite 5 days a week while managing and supporting AWS-based data pipelines and applications. You will be knee-deep in core AWS infrastructure, building out ETL workflows, and keeping critical data moving reliably.
What You'll Actually Be Doing
Your day-to-day will revolve around building, optimizing, and troubleshooting cloud-native data pipelines. You'll spend a lot of time writing Python and PySpark code, configuring AWS services like Glue and EMR, and setting up orchestration tools to keep jobs running smoothly. Expect to spend a solid chunk of your day debugging failed runs in CloudWatch, tweaking IAM policies, and ensuring your ETL processes across tools like IICS or IDMC are processing data efficiently without choking the databases.
The Core Tech Stack
AWS is the backbone here, so you need to be comfortable spinning up and debugging resources like S3, Lambda, Step Functions, RDS, and VPC without constantly Googling the documentation. Python and PySpark are non-negotiable since you'll be processing heavy data loads. On top of that, you'll need a firm grip on SQL, Linux shell scripting, and traditional ETL tools. If you don't know how to trace a bottleneck through CloudWatch logs or optimize a sluggish Spark job, you're going to have a rough time.
Interview Expectations
When you sit down with the technical panel, expect them to ask you to design a fault-tolerant, scalable ETL pipeline using S3, Glue, and Lambda, while explaining how you handle error states and retries using Step Functions. They are secretly looking for architectural resilience and practical debugging skills, not just textbook definitions. You'll also likely be grilled on how you optimize PySpark jobs and manage shuffle partitions when dealing with large datasets, so be ready to talk through a real production performance issue you've solved.
Application Advice
To get past the ATS and catch the recruiter's eye, make sure your resume explicitly highlights your hands-on experience with AWS data pipelines, Python/PySpark, and scheduling tools like Control-M or Auto-Sys. Don't just list technologies; write brief bullet points detailing the scale of the data you've handled and how you used CloudWatch or the ELK stack to monitor and troubleshoot production environments. Dropping specific service names mentioned in the job description will heavily boost your keyword match.