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Tier-1 brand, metro location, mid-level generalist role increases applicant competition.
Core data engineering skills are highly transferable across industries, so background fit is low.
Explicit 4–6 years plus mandatory Databricks, Spark, AWS, Python, and Java increases filter strictness.
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Design and build scalable ETL processes and data pipelines integrating diverse data sources using Databricks on AWS and Spark.
Develop and optimize Lakehouse architectures and big data access patterns for efficient large-scale data processing.
Drive automation and efficiency through CI/CD pipelines, Infrastructure as Code, DevOps practices, and enhance data governance and security.
4 to 6 years of experience in data engineering and ETL pipeline development.
Hands-on experience with Databricks on AWS and Spark streaming.
Proven skills in designing and implementing scalable data warehousing solutions with AWS data services such as DynamoDB, Glue, Athena, EMR, Redshift, Lambda, and Kinesis.
Proficiency in Python programming including PySpark/Spark SQL and Java.
Experienced with cloud-based big data environments and modern data engineering tools and techniques, especially AWS and Databricks.
Comfortable working in Agile and DevOps environments with emphasis on automation and CI/CD pipelines.
Able to independently drive improvements in data platform performance, governance, and security at scale.