Job Description & Details
This is a heavy-hitting architecture role focused on tearing down legacy on-prem systems and building out a modern, cloud-first data ecosystem. If you enjoy untangling massive enterprise data sprawls and defining the future-state data platform for a large organization, this gig will keep you on your toes.
What You'll Actually Be Doing
Your day-to-day will be a mix of high-level strategy and deep technical design, figuring out how to move petabytes of data without breaking production. You'll be collaborating with engineering teams to migrate legacy workloads from traditional data warehouses and Hadoop clusters into a scalable cloud environment. Expect to spend a lot of time defining architectural standards, setting up governance frameworks, and ensuring your streaming and batch pipelines can actually handle enterprise loads.
The Core Tech Stack
You need to be completely fluent in the modern big data ecosystem, specifically Databricks, Apache Spark, and Apache Kafka for real-time streaming. Because you'll be bridging the old world and the new, heavy exposure to AWS is mandatory alongside experience migrating off legacy systems like Teradata, Vertica, or Oracle. On the coding side, expect to lean heavily on Java, Python, and advanced SQL while designing distributed systems that won't fall apart under pressure.
Interview Expectations
They are going to test your ability to design resilient, petabyte-scale systems by asking you to walk through a real-time migration scenario from an on-prem Hadoop cluster to a cloud lakehouse. The hiring manager isn't just looking for a list of tools you've used; they want to hear about your failure modes, how you handled massive data skew, and how you ensured zero downtime for downstream consumers during the cutover.
Application Advice
Skip the generic project summaries and make sure your resume immediately highlights massive cloud migrations you've personally architected. Explicitly list your scale—mention terabytes or petabytes—and weave in keywords like Databricks, Spark, Kafka, and AWS right at the top so you can easily clear the initial ATS filters.