Job Description & Details
This is a heavy-duty data engineering role disguised as a Java and Node gig, requiring you to wrangle massive enterprise datasets while dipping your toes into modern GenAI frameworks. If you like bridging the gap between traditional big data architectures like Hadoop and modern cloud pipelines on GCP, this contract gives you a chance to touch almost every modern data tool out there. It's fast-paced, expects local presence in one of their primary hubs, and demands a polyglot engineer who doesn't panic when looking at Spark streaming logs.
What You'll Actually Be Doing
You'll spend your days designing and maintaining scalable data pipelines, migrating legacy on-prem Hadoop and Hive workloads over to GCP, and spinning up real-time streaming architectures using Kafka, Flink, and Spark. Half your battle will be orchestrating workflows with Airflow or Cloud Composer, while the other half involves figuring out how to plug emerging GenAI tools like LangChain and LangGraph into existing data flows. Expect to troubleshoot streaming bottlenecks, enforce data governance at scale, and occasionally hack together quick UI features in React and Node.
The Core Tech Stack
You absolutely must know your way around Python, PySpark, and Apache Kafka inside and out, because you'll be building streaming pipelines that can't afford to drop packets. GCP is the cloud home here, so deep familiarity with BigQuery, Dataflow, and GCS is non-negotiable, alongside a strong foundation in NoSQL and data lakehouse formats like Iceberg or Delta Tables. If you bring solid Java or NodeJS coding chops paired with any real-world exposure to building agent-based data capabilities using LangChain, you'll easily stand out from the purely traditional ETL crowd.
Interview Expectations
The tech screen will likely grill you on handling backpressure and state management in Flink or Spark streaming, so be ready to explain how you've debugged memory leaks in high-throughput JVM environments. The hiring manager is also going to test your practical understanding of GenAI integration by asking how you'd safely connect a LangGraph agent to a secure enterprise data lake without introducing massive latency or hallucinations.
Application Advice
Make sure your resume doesn't just read like a generic Java list; put your GCP certifications and hands-on streaming experience right at the top. Explicitly call out keywords like Apache Kafka, PySpark, Apache Iceberg, and any GenAI frameworks you've tinkered with so you can easily clear the initial ATS filters for this hybrid setup.