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Remote option, popular Data Engineer title, metro locations, and broad Big Data/AWS skill requirements increase competition.
Big Data and cloud skills transfer broadly, but fraud and banking compliance preferences raise domain specificity.
Multiple explicit years requirements, specific Big Data/AWS/Scala skills and regulated banking/fraud preferences increase rigor.
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Design and implement scalable, fault-tolerant big data and cloud-based data applications handling terabytes of data for enterprise fraud analytics.
Collaborate with architects, data scientists, and product teams to translate business needs into technical solutions within a Scaled Agile framework.
Own full lifecycle development including design, coding, testing, deployment using AI, AWS (EMR, S3, Glue, Redshift), Spark, Hive, Kafka, Scala, Python, and SQL technologies.
Bachelor's degree in Computer Science or similar technical field required; alternatively, 7+ years of full lifecycle software development experience without a degree.
Minimum 5+ years of experience in full lifecycle software development with coding, design, testing, and performance tuning.
6+ years of experience with Big Data, Cloud, and database technologies including AWS, Spark, Hadoop, Hive, Cassandra, Graph DB, MySQL.
1-2 years experience working with AI technologies.
Experienced in backend data pipeline development for fraud or cybersecurity analytics in regulated financial services environments.
Comfortable working in a distributed team with US Eastern Time aligned working hours and agile methodologies.
Proven ability to translate business domain concepts related to fraud analytics into complex, secure, scalable software solutions utilizing modern big data and cloud technologies.