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Popular AWS data engineer title, mid-level role, and metro hiring increase candidate competition.
Core cloud data engineering skills transfer across industries, though finance domain knowledge adds moderate specificity.
Many mandatory AWS, Spark, Iceberg, and Kafka skills imply moderately strict technical filters.
Design and implement scalable, secure, and cost-optimized AWS data architectures integrating multiple internal and external financial data sources.
Develop and maintain ETL pipelines, building and optimizing applications using Apache Spark (PySpark) and Apache Iceberg on AWS data lakes.
Implement event-driven workflows and federated querying solutions using AWS Step Functions, EventBridge, Starburst, and automate deployments with CI/CD pipelines.
Strong hands-on experience with AWS services: Lambda, Glue ETL, Athena, S3, DynamoDB, Step Functions, EventBridge, SNS, SQS.
Proficiency in Apache Spark development using PySpark, including unit testing and performance tuning.
Experience working with Apache Iceberg for data lake design and optimization.
Work Experience Required: Not explicitly mentioned in the JD
Experienced in designing and operating enterprise-scale AWS data architectures and event-driven data workflows in fast-paced, high-visibility Finance programs.
Skilled in integrating modern big data technologies including Spark, Iceberg, Starburst, and streaming platforms like Apache Kafka.
Comfortable with building generic, reusable data engineering solutions and optimizing performance in complex data lake environments.