About
CTI Staffing is partnering with a leading financial services organization to find a Senior Data Engineer for their data aggregation team based in Durham, NC. This team builds external-facing data solutions that bring together retail, clearing, and custody data to power financial goal planning, investment advice, and projection tools used by partner organizations. It's high-visibility work where data reliability, security, and partner integration quality all matter. What You'll Do: Design and build ETL pipelines in Scala/Spark to consolidate and move data across systems Develop and maintain Java clients consuming REST and SOAP APIs for external partner integration Schedule, monitor, and debug ETL Spark batch jobs running on AWS EMR Manage data flow across AWS services including S3, Lambda, SNS, IAM, and KMS Write to and maintain NoSQL datastores (Cassandra or Yugabyte) Support Snowflake-based data warehousing workflows Work with large dataset storage formats such as Parquet and HDF5 Schedule and monitor production jobs using Control-M Collaborate across teams on data architecture decisions and code reviews Requirements: What You Bring: Must-Have: Bachelor's degree in Computer Science, Information Systems, or related field 8+ years of data engineering experience (10+ preferred), with proven Spark Services development for data movement Hands-on Scala development for Spark-based ETL pipelines (not Python-based) Java experience consuming REST and SOAP APIs in data pipeline contexts Production experience with AWS EMR running Spark batch jobs Working knowledge of AWS S3, Lambda, IAM, KMS, and SNS Experience with Cassandra or Yugabyte (or comparable NoSQL datastore) Experience with Snowflake or a comparable cloud data warehouse Experience with large dataset storage formats (Parquet, HDF5, or similar) Nice-to-Have: Python scripting experience Background in financial services or other regulated data environments Experience with enterprise job scheduling tools (Control-M or similar) Experience building external-facing/partner-facing data integrations Technical Environment: Scala, Java, Apache Spark, AWS (EMR, S3, IAM, KMS, Lambda, SNS, RDS), Cassandra/Yugabyte, Snowflake, Control-M, Parquet, HDF5 What Success Looks Like: Independently owns Spark batch job development and troubleshooting within the first 90 days Integrates external API consumption cleanly into existing pipelines Demonstrates fluency across the AWS data stack without ongoing supervision
Languages
- English
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