The Scion Group
Data Engineer
Chicago, Illinois, United StatesFrom $135kmidAdded today
About this role
The Scion Group is hiring a Data Engineer in Chicago to design and maintain production-grade ELT pipelines and analytics models using Snowflake and DBT. This role requires deep SQL and DBT expertise, with ownership of end-to-end data systems from ingestion through transformation and BI consumption.
What you'll do
- Design and implement end-to-end ELT pipelines into Snowflake from APIs, SaaS platforms, and internal databases
- Build and maintain Change Data Capture (CDC) processes with incremental loading and idempotency
- Design layered DBT models and write high-quality SQL transformations optimized for cost and performance
- Implement data validation, anomaly detection, and monitoring to ensure production dataset quality
- Independently diagnose and resolve pipeline failures and data quality issues
- Collaborate with analytics and business stakeholders to translate requirements into scalable solutions
What they're looking for
- Advanced SQL (window functions, performance tuning, complex joins)
- DBT (data modeling, incremental models, testing strategies)
- Snowflake or similar cloud data warehouse
- ELT pipeline design and maintenance
- Change Data Capture (CDC) implementation
- Data validation and monitoring frameworks
- Git-based workflows and CI/CD (GitHub Actions, dbt Cloud preferred)
- Looker or similar BI tools
Benefits
- Health Insurance
- Dental Insurance
- Vision Insurance
- 401k Matching
- Paid Maternity Leave
- Discretionary annual end-of-year bonus
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The Scion Group
The Scion Group operates corporate and student housing communities and is hiring Full Stack Web Developers to enhance and maintain their housing websites using WordPress, focusing on user experience and web performance.
View all jobs at The Scion GroupLikely interview questions
- Walk us through a complex ELT pipeline you built from scratch—what were the key challenges and how did you handle data consistency?
- Tell us about a time you identified a production data issue before stakeholders noticed it. How did you investigate and resolve it?