Data Engineer

The Risk & Fraud team at Q2 helps customers take a proactive stance against fraud while managing the risks inherent to their business. We build and enhance products that evolve with the ever-changing fraud landscape, delivering tangible value to our customers. The Role 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. Responsibilities 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 Production Support: review production data pipeline executions, investigate and resolve failures Development: build and orchestrate data pipelines, defining data flow, transformations, and dataset relationships Observability: monitor and optimize data pipelines for performance and efficiency Collaboration: work closely with teams and stakeholders to understand data requirements and ensure platform solutions meet business needs Requirements Must Haves Typically requires a Bachelor’s degree in a relevant field 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 Nice to Have Experience with tools such as Apache Airflow, dbt, Kafka, Airbyte, or FiveTran Experience with Snowflake or similar cloud data warehouses Experience with SQL Server, PostgreSQL, or NoSQL 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 Health & Wellness Hybrid Work Opportunities Flexible Time Off Career Development & Mentoring Programs Health & Wellness Benefits, including competitive health insurance offerings and generous paid parental leave for eligible new parents Community Volunteering & Company Philanthropy Programs Employee Peer Recognition Programs – “You Earned it” We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, or veteran status. #J-18808-Ljbffr