Senior Data Engineer: Snowflake & AWS Pipelines

Kaleidoscope Innovation, Inc.; Detroit (MI); On-site; USD 120,000 - 180,000; Full time

Job summary

Kaleidoscope Innovation, Inc. is seeking a Senior Data Engineer in Detroit to design, build, and optimize scalable data pipelines and platforms supporting analytics, reporting, and AI/ML use cases. Strong Snowflake on AWS, Python ETL/ELT, and Control-M experience are essential.

The role involves collaboration across engineering, analytics, and business teams in an Agile delivery model, with responsibilities spanning data modeling, governance, and performance tuning.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field.
• 9+ years of data engineering experience including enterprise-grade data platforms.

Responsibilities

• Design, develop, and maintain end-to-end data pipelines (batch and near real-time) using Snowflake, AWS, and Python.
• Build data models in Snowflake for analytics and downstream consumption.
• Develop and maintain ETL/ELT workflows with Python and IBM DataStage.
• Implement job scheduling and monitoring with Control-M; manage SLAs and dependencies.
• Ensure data quality, governance, lineage, and documentation across pipelines.
• Tune performance and control costs for Snowflake and AWS.
• Collaborate with Data Science/AI, BI, Product, and Platform teams.
• Participate in Agile ceremonies and SDLC/change management.
• Troubleshoot production issues and drive preventative improvements.

Skills

Agile/Scrum

Communication

Cross-team coordination

Ownership

Education

Bachelor’s degree in Computer Science, Engineering, or related field

Tools

Snowflake

AWS

Python

Control-M

IBM DataStage

SQL

Git

CI/CD

Job description

Kaleidoscope Innovation, Inc. is seeking a Senior Data Engineer in Detroit to design, build, and optimize scalable data pipelines and platforms supporting analytics, reporting, and AI/ML use cases. Strong Snowflake on AWS, Python ETL/ELT, and Control-M experience are essential.

The role involves collaboration across engineering, analytics, and business teams in an Agile delivery model, with responsibilities spanning data modeling, governance, and performance tuning.