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High due to strong brand, remote role, and broad mid-level data engineering skillset.
Low because core data engineering skills like Spark, ETL, and cloud are highly transferable across industries.
Medium because of explicit years plus required Spark, cloud, and data pipeline skills but flexible certification expectations.
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Collaborate with client-facing teams to gather and analyze technical requirements, contributing to solution design and implementation.
Develop and deliver production-quality code following best practices, including unit and integration testing, focusing on data management and distributed computing solutions.
Research and prototype new technologies to enhance solution offerings and participate in agile development processes including code reviews and scrum ceremonies.
Minimum 2 years of relevant hands-on experience in software engineering or related roles.
Strong foundation in computer science fundamentals including data structures and programming.
Proficiency in at least one programming language among Python, Java, or Scala and experience with distributed computing frameworks like Spark preferred.
Experience working with cloud platforms such as AWS, Azure, or GCP; understanding of RDBMS and familiarity with ETL processes preferred.
Technically skilled in building data pipelines and orchestration for data management solutions leveraging distributed computing and cloud services.
Experienced in agile environments with strong analytical and problem-solving capabilities to deliver scalable, production-ready software.
Comfortable working in a client-facing context with readiness to travel globally and collaborate across diverse teams and locations.