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Metro location and known global employer raise applicant density despite senior specialized skills.
High — specialized Spark and distributed data platform expertise limits cross-industry transferability.
High — explicit 10+ years and deep Apache Spark, cloud, and distributed systems expertise required.
Design and lead architecture of scalable, resilient Apache Spark-based distributed data processing solutions.
Provide technical leadership and guidance on Spark performance optimization, operational best practices, and solution design across engineering teams.
Collaborate with stakeholders to translate business requirements into technical architectures, and drive cloud transformation and platform modernization initiatives.
10+ years of experience in software or data engineering focusing on distributed systems and big data.
Strong hands-on expertise with Apache Spark (Spark SQL, Structured Streaming, DataFrames, Dataset APIs).
Proficiency in Scala, Java, or Python programming languages.
Experience deploying and operating Spark solutions on AWS and/or GCP platforms in batch and streaming data pipelines.
Individual contributor with demonstrated architecture leadership in large-scale distributed data platforms and Spark-based solutions.
Experience managing terabyte-to-petabyte scale data processing environments and applying advanced Spark optimization techniques.
Comfortable collaborating cross-functionally with product managers, engineering leads, and data scientists to align technical solutions with business goals.