Data Engineer

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

We are unable to sponsor or take over sponsorship of an employment visa at this time.

Job Title: Senior Data Engineer

We are looking for a Senior Data Engineer to design, build, and optimize scalable data pipelines and data platforms supporting analytics, reporting, and AI/ML use cases. The ideal candidate has strong hands-on experience with Snowflake on AWS, Python-based ETL/ELT development, and enterprise scheduling/orchestration tools like Control-M, along with legacy/enterprise ETL experience in IBM DataStage. You will collaborate across engineering, analytics, and business teams in an Agile delivery model.

Key Responsibilities

• Design, develop, and maintain end-to-end data pipelines (batch and near real-time) using Snowflake, AWS services, and Python.
• Build and optimize data models in Snowflake (e.g., dimensional modeling, data vault, or curated data marts) for analytics and downstream consumption.
• Develop and maintain ETL/ELT workflows using Python and IBM DataStage; migrate/modernize workloads where applicable.
• Implement job scheduling, monitoring, and operational support using Control-M (alerting, retries, SLAs, and dependency management).
• Ensure data quality, governance, lineage, and documentation standards are met across pipelines.
• Perform performance tuning and cost optimization across Snowflake and AWS (query optimization, clustering, warehouse sizing, storage management).
• Partner with stakeholders (Data Science/AI, BI, Product, and Platform teams) to enable data products and AI-ready datasets.
• Participate in Agile ceremonies, contribute to estimation, planning, and sprint execution; follow SDLC and change management processes.
• Troubleshoot production issues, perform root-cause analysis, and drive preventative improvements.

Required Technical Skills

• Snowflake: Strong expertise in Snowflake architecture, SQL development, performance tuning, security/roles, data loading/unloading, and best practices.
• AWS: Hands-on experience with AWS data ecosystem (commonly S3, IAM, CloudWatch; plus services such as Glue, Lambda, EC2, Step Functions, EMR, or Kinesis as applicable).
• Python: Strong Python programming for data engineering (ETL/ELT frameworks, API ingestion, automation, unit testing, logging).
• Control-M: Experience designing and managing enterprise job scheduling, dependencies, calendars, SLAs, monitoring, and incident handling.
• IBM DataStage: Solid experience building and maintaining DataStage jobs, handling complex transformations, and supporting production workloads.
• SQL: Advanced SQL skills for transformations, optimization, and data validation across large datasets.
• CI/CD & Version Control: Experience with Git and CI/CD practices for data pipelines (tools may vary).
• Operational Excellence: Monitoring, alerting, and production support experience in a 24x7 or business-critical environment.

Good to Have

• AI/ML exposure: Experience enabling AI/ML pipelines or feature datasets; familiarity with ML lifecycle concepts, feature engineering, or MLOps tools/processes.
• Experience with data governance/metadata tools and practices (catalog, lineage, data quality frameworks).
• Exposure to streaming or event-driven architectures.

Required Soft Skills

• Strong experience working in Agile/Scrum teams and delivering within structured SDLC processes.
• Excellent communication skills (technical and non-technical) with the ability to explain complex data concepts clearly.
• Proven ability to coordinate across multiple teams (Data Engineering, Data Science, DevOps, Security, BI, and business stakeholders).
• Strong ownership mindset, problem-solving ability, and attention to detail.

Qualifications (Typical)

• Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent practical experience).
• 9+ years of data engineering experience, including enterprise-grade data platform delivery and production support.

Kaleidoscope, an Infosys Company, is an equal opportunity employer, and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, spouse of protected veteran, or disability.