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Mid-level Data Engineer, metro location, common title and broad skillset create high competition.
Core data engineering skills are transferable, though fraud/financial domain experience is moderately preferred.
Explicit 5–9 years requirement plus a long mandatory tech stack makes shortlisting highly strict.
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Own the creation and delivery of high-quality, resilient data solutions primarily for Fraud business partners in a financial services context.
Work across on-prem relational, big-data, and cloud technologies (AWS, Azure) to develop and maintain scalable batch and streaming data pipelines.
Engage fully in Agile Scrum team activities including full software development lifecycle and collaboration with wider teams.
5+ years of relevant work experience in data engineering roles.
Strong programming skills in Python and batch data processing using Apache Spark.
Proven expertise in SQL Server, Hadoop, and SQL programming.
Experience with job scheduling tools like Autosys and working in Linux environments with Python/Shell scripting.
Experienced in building data solutions that support both BI and Data Science use cases, including data mart dimensional modeling.
Familiarity with real-time data integration and processing technologies such as Kafka, MongoDB, Neo4j, and cloud services like Azure Databricks or AWS Glue.
Comfortable working in Agile environments with exposure to software development best practices and version control systems.