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Strong employer brand, mid-level generalist data role, metro location, and common tech stack increase competition.
Core data engineering skills (AWS, Databricks, PySpark) are highly transferable across industries.
Explicit 3-5 years requirement plus mandatory AWS/Databricks/PySpark skills make filters strict.
Develop and maintain AWS cloud-native data lakes and ecosystems supporting enterprise functions.
Build and manage scalable data pipelines using Databricks Lakehouse architecture, Delta Lake, Unity Catalog, and Databricks Workflows.
Optimize Databricks development including PySpark/SQL coding, cluster/job configuration, and performance tuning for production-grade solutions.
3-5 years experience in IT developing AWS cloud-native data lakes and ecosystems with some production support experience.
Proficient in AWS native services (Glue Studio, Athena, Redshift, Postgres DB) and programming skills in Python and Spark.
Experience with SQL and databases such as MySQL, PostgreSQL; knowledge of data security and privacy best practices.
1-2 years experience in onshore-offshore delivery model; functional knowledge of enterprise functions is a plus.
Hands-on expertise in Databricks Lakehouse architecture and optimization for large data pipelines.
Strong programming background with Python, PySpark, and SQL in cloud environments, especially AWS.
Experience operating in a global, dynamic delivery setting with good communication and collaboration skills.