Data Engineer

As passionate about our people as we are about our mission

Q2 is a leading provider of digital banking and lending solutions to banks, credit unions, alternative finance companies, and fintechs in the U.S. and internationally. Our mission is simple: build strong and diverse communities through innovative financial technology—and we do that by empowering our people to help create success for our customers.

Q2 is a leading provider of digital banking and lending solutions to banks, credit unions, alternative finance companies, and fintechs in the U.S. and internationally. Our mission is simple: build strong and diverse communities through innovative financial technology—and we do that by empowering our people to help create success for our customers.

Being as passionate about our people as we are about our mission. We celebrate our employees in many ways, including our “Circle of Awesomeness” award ceremony and day of employee celebration among others. We invest in the growth and development of our team members through ongoing learning opportunities, mentorship programs, internal mobility, and meaningful leadership relationships. We also know that nothing builds trust and collaboration like having fun.

We hold an annual Dodgeball for Charity event at our Q2 Stadium in Austin, inviting other local companies to play, and community organizations we support to raise money and awareness together.

In this role, you will take ownership for building and operating our data architecture to support new and evolving fraud solutions. You’ll play a key role in ensuring data is reliable, scalable, and accessible to power models, agents, and UIs directly impacting our customers’ ability to detect and prevent fraud.

This is an opportunity to work on production systems with real-world impact while continuing to grow your skills in data engineering, cloud platforms, and distributed systems.

• Design, build, and maintain scalable data pipelines and workflows in a cloud environment
• Deliver clean, well-structured datasets to support fraud analytics, machine learning models, and agentic solutions
• Contribute to improving our data architecture, including ingestion, storage, and access patterns
• Own data operations by monitoring data workflows, triaging failures, and resolving data issues
• Enhance observability and performance by implementing monitoring and optimizing pipelines for reliability, scalability, and cost efficiency
• Partner with product managers, data scientists, and engineers to translate fraud and risk requirements into data solutions
• Write maintainable code; participate in code reviews; and help improve testing, deployment, and documentation standards

• Typically requires a Bachelor’s degree in (relevant degree) and a minimum of 2 years of related experience; or an advanced degree without experience; or equivalent work experience.
• Experience building and maintaining data pipelines and workflows in production environments
• Proficiency in SQL and working with relational and/or analytical data stores
• Experience with Python
• Familiarity with data modeling, transformation, and orchestration concepts
• Experience with data warehouses and distributed data processing systems
• Experience with version control (e.g., Git) and CI/CD practices
• Ability to troubleshoot data issues, debug pipelines, and work through ambiguous problems

• Experience with tools such as Apache Airflow, dbt, Kafka, Airbyte, or Five Tran
• Experience with Snowflake or similar cloud data warehouses
• Experience with SQL Server, PostgreSQL, or No
  
  SQL systems like DynamoDB
• Familiarity with infrastructure as code tools (e.g. Terraform)
• Experience with Docker and/or Kubernetes
• Exposure to platforms like Databricks, AWS Glue, AWS Sagemaker, Snowpark

• Production Support:
  Start the day by reviewing production data pipeline executions, investigating and resolving failures
• Development:
  Build and orchestrate data pipelines, defining data flow, transformations, and dataset relationships
• Observability:
  Monitor and optimize data pipelines for…