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Popular data role and metro hiring increase competition, but niche CA7/Control-M requirement reduces it.
Medium—core data engineering skills transfer widely, but on-prem Hadoop and CA7 specialization reduce portability.
High—explicit 7–10 years plus mandatory Hadoop, Spark, Python, and enterprise scheduler requirements.
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Design, build, and maintain scalable data pipelines and processing systems in an on-premises Big Data environment.
Own architectural and design decisions; set technical standards and direction for the data engineering team.
Mentor and support other data engineers through code reviews, technical coaching, and problem-solving.
7–10 years of overall experience in data engineering with proven technical leadership experience.
Strong hands-on development experience with Python, Apache Spark, and Big Data / Hadoop ecosystem (Hadoop, Hive, Impala, HDFS) in on-premises environments.
Experience with job scheduling/orchestration tools such as CA7 or Control-M.
Exposure to AI/ML concepts is required; willingness to learn and grow in this area.
Experienced in leading technical teams and owning architecture/design decisions in complex, large-scale data environments.
Highly skilled in building and optimizing data workflows using Python, Spark, and Hadoop components with a focus on on-premises setups.
Practiced in collaborating with cross-functional teams to deliver reliable data solutions and troubleshoot complex pipeline issues at scale.