Sr. Data Engineer - Hybrid Chicago

We are partnered with a growth-stage fintech company building transparent, borrower-friendly financial products. The team is simplifying access to credit through data-driven decision-making, modern infrastructure, and a strong emphasis on customer outcomes. You will join a small, collaborative, and impact-oriented department.

• Design, build, and maintain scalable data pipelines and infrastructure using Snowflake, Airflow, Fivetran, and dbt
• Develop and optimize SQL- and Python-based workflows for data ingestion, transformation, and automation
• Partner with business stakeholders to design and maintain reliable data marts, addressing data quality gaps and inconsistencies
• Implement robust testing and validation frameworks, particularly within dbt models, to ensure end-to-end data reliability
• Apply data platform reliability best practices, including monitoring and alerting (e.g., Datadog) for ETL workflows
• Collaborate cross-functionally to translate business needs into scalable data solutions
• Own projects end to end while contributing effectively within a collaborative team environment

• 3 - 5 years of experience in data engineering or analytics engineering roles (fintech or regulated environments preferred)
• Bachelor’s degree in a relevant field or equivalent practical experience
• Strong proficiency in SQL
• Hands-on experience with Python, Snowflake, Airflow, Fivetran, and dbt
• Solid understanding of data modeling, version control (Git), and CI/CD workflows
• Experience operating data platforms in cloud environments, preferably AWS
• Strong communication skills, business intuition, and the ability to troubleshoot complex data issues