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Senior specialized data role in metro locations with common Data Engineer title yields moderate candidate competition.
Cloud-native data engineering skills on AWS and Databricks are broadly transferable across industries.
Multiple mandatory technical skills and explicit 8+ years requirement tighten shortlisting.
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Design, develop, implement, optimize and maintain complex data engineering solutions using AWS and Databricks technologies.
Build and manage data pipelines with PySpark or Spark Scala, including streaming pipelines for near real-time analytics.
Work with data warehouse, data lake, and lake-house architectures, including use of AWS services like Glue, Lambda, Redshift, EMR, and Kinesis.
8+ years of hands-on experience in data engineering design, development, and implementation.
Strong proficiency in AWS data engineering services (Glue, Lambda, Step Functions, Redshift, EMR, Kinesis).
Experience with Databricks and PySpark or Spark Scala for data pipelines; understanding of streaming data pipelines.
Strong SQL development skills with query optimization; experience with at least one NoSQL database; Python programming skills.
Experienced senior data engineer with advanced understanding of modern data architectures including data warehouse, lake, and lake-house patterns.
Operates with strong DevOps practices including CI/CD pipelines and version control using Git for data engineering solutions.
Strategic understanding of data governance and data platform design, comfortable with implementing scalable, near real-time analytics solutions.