Job Description - Data Engineer


About This Opportunity

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







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