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Responsibilities:

  • Own dashboard changes end to end in QuickSight and ThoughtSpot — new metrics and filters, SPICE refresh management, internal-to-production promotion, and post-release validation.
  • Build and maintain batch and streaming ETL pipelines on AWS using Glue (PySpark), S3, Redshift, and Airflow (MWAA) DAGs.
  • Support near-real-time ingestion (Kafka/MSK CDC → Glue Streaming → S3 → Redshift).

Required Skills:

  • 3-4 years of experience in data engineering and/or analytics engineering.
  • Strong SQL on Redshift and solid Python/PySpark.
  • Hands-on experience with the AWS data stack: S3, Glue, Redshift, CloudWatch.

Team Culture:

  • You will work with a Data Engineering & Analytics team that builds product context quickly and collaborates across product, engineering, DevOps, and customer-facing teams.
  • The role emphasizes listening well, working independently, and making the most of AWS infrastructure to deliver analytics that matter to credit union customers.

Eltropy

Eltropy is a digital conversations platform for credit unions and community financial institutions in the US. The Data Engineering & Analytics team builds AWS data pipelines and customer-facing dashboards that power analytics; they seek a collaborative engineer who learns fast and works across teams.

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