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Mid-level generalist data role with 3+ years and metro location increases competition.
Core data engineering skills (AWS, Spark, Python, SQL) are highly transferable across industries.
Explicit 3-year minimum plus mandatory AWS, Spark, Python and CI/CD requirements increase filter strictness.
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Design and develop data ingestion patterns and automate data pipelines for IntelDS Raw and Unified data layers using AWS technologies.
Implement DevSecOps practices including automation of integration, delivery, and testing of data pipelines in a cloud environment.
Analyze and optimize data models and ingestion processes to enhance data availability and support client applications with data consumption.
Minimum 3 years of experience as a data engineer with Big Data and Data Lakes in a cloud environment.
Proven skills with AWS services including at least 3 of: RedShift, S3, EMR, Cloud Formation, DynamoDB, RDS, Lambda.
Experience with big data technologies such as Spark, Presto, or Hive.
Proficiency in Python and SQL, with good understanding of data warehousing and data modeling concepts.
Experienced in designing and automating complex data pipelines and ingestion patterns within cloud-based Big Data platforms.
Strong orientation towards DevSecOps practices including CI/CD and automated testing in data platform contexts.
Capability to work collaboratively across diverse teams while managing multiple priorities in a demanding environment.