This is a 12-month contract role with potential for full-time conversion. This is a hybrid position (3-4 days in office), with the work location in Minneapolis, MN. Applicants must be authorized to work in the U.S. without the need for sponsorship.
We are seeking a Senior Data Engineer to join a fast-paced, growing data engineering team within a forward-thinking investment firm. This role is ideal for a technically skilled data professional with strong experience in SQL and Python, and a passion for building data pipelines, modeling, and delivering modern data solutions. You must be comfortable working with both structured and unstructured data, and have a strong interest in cloud technologies. While experience with AWS, DBT, and Snowflake is a plus, the team is open to training the right candidate in these tools.

REQUIRED QUALIFICATIONS

• Strong experience with SQL and Python.
• Ability to work confidently with structured and unstructured data.
• Strong analytical thinking and problem-solving skills.
• Excellent written and verbal communication skills.
• Self-starter who is eager to learn and grow in a fast-moving environment.

NICE TO HAVE SKILLS

• Experience with DBT (Data Build Tool).
• Familiarity with Snowflake or similar cloud data platforms.
• Exposure to AWS services (e.g., S3, Redshift, Glue, Lambda).
• Experience with data visualization tools such as Looker (training provided).
• Knowledge of version control (Git), CI/CD, and workflow orchestration tools like Airflow.
• Background in financial services or data-intensive business environments.
  Job Types: Full-time, Contract
  Pay: $60.00 - $80.00 per hour
  Work Location: Hybrid remote in Golden Valley, MN 5542

• Build and maintain scalable data pipelines using SQL and Python.
• Work with both structured and unstructured data to support analytical and operational use cases.
• Support cloud data engineering projects, especially those leveraging AWS (training available).
• Collaborate on data modeling and transformation efforts across teams.
• Assist with documentation, QA, and code reviews to ensure data quality and system reliability.
• Communicate effectively with engineers, analysts, and business partners to deliver timely data solutions.
• Continuously identify opportunities to optimize and improve data workflows.