Job Description & Details
This is a heavy-hitting modernization role where you'll be tearing down legacy infrastructure and rebuilding it on modern cloud architecture. If you enjoy solving massive data scaling problems and leading migrations from EMR to Databricks and Snowflake, this gig will keep you thoroughly engaged.
What You'll Actually Be Doing
You'll spend your days designing, building, and scaling enterprise-grade data platforms while driving large-scale migrations from legacy systems like EMR over to Databricks and Snowflake. Expect to handle complex data ingestion pipelines from external partners, mentor junior engineers, and collaborate closely with stakeholders to align technical direction with business needs.
The Core Tech Stack
You absolutely need deep, production-level experience with Databricks, Snowflake, and Apache Spark, backed by robust programming skills in either Java or Python. The team relies heavily on these tools to process petabyte-scale data efficiently, so knowing how to optimize Spark jobs and tune Snowflake queries is non-negotiable for keeping performance high and costs low.
Interview Expectations
You will likely be grilled on how you handle data skew and memory management during large-scale Apache Spark transformations. The hiring manager wants to see if you can debug performance bottlenecks under pressure rather than just throwing compute at the problem. You should also expect deep-dive questions around migration strategies, specifically how you safely transition critical data workloads from EMR to Databricks or Snowflake without dropping data or breaking downstream consumers.
Application Advice
Make sure your resume puts your hands-on migration experience front and center, explicitly highlighting projects where you moved workloads from EMR to Databricks or Snowflake. Weave in keywords like "enterprise data ingestion," "pipeline modernization," and specific metrics showing how you optimized Spark or SQL performance to ensure you easily clear the initial ATS screening.