SDET Ex- Capital One

Solutions QA Engineers to help harden the production experience of our Slingshot platform a modern data optimization product used by large enterprise data teams. This role is not traditional QA.

You ll be hunting real-world customer failures that slip through happy-path test coverage, especially those stemming from 3rd-party integration edge cases. You ll work directly with product, engineering, and support to identify bugs that impact reliability and customer trust.

Your focus: explore, test, and document failure modes across API, CLI, and orchestration workflows using modern data stack tools (Snowflake, Databricks, Airflow, Fivetran, Terraform, DBT, Azure Data Factory, AWS, GitHub Actions etc.). This is a manual-first role to discover the problem space automation may follow as patterns emerge.
Key Responsibilities

• Execute exploratory, production-oriented QA on customer-critical integration paths across tools like DBT, Airflow, Terraform, ADF, and GitHub Actions
• Recreate and document integration issues surfaced by customers and support
• Test and validate APIs, CLI, and system behavior under different configurations
• Partner with Support and Product to prioritize edge case testing aligned with real-world impact
• Identify and articulate blast radius and system behavior under unexpected or invalid configurations
• File actionable, reproduction-ready bugs with context for engineering
• Track coverage across tested tools, connectors, and orchestration flows
• Contribute to early-stage runbooks and automation plans, once common patterns are identified
• Serve as an internal integration hunter to expose gaps before customers do

Required Qualifications

• years experience in software QA, support engineering, or technical testing roles
• Familiarity with modern data engineering platforms and orchestrators:
• Examples: Airflow, Terraform, DBT, Databricks, GitHub Actions, Azure Data Factory
• Ability to manually set up test environments and reproduce issues across multiple systems
• Experience with APIs, CLI tools, and validating system integrations
• Strong written skills for defect reproduction, test case documentation, and QA communication
• Self-starter mindset able to explore unfamiliar systems, dig for failure paths, and propose next steps
• Ability to work independently in a fully remote team
• Comfortable testing in complex, high-variance customer environments (non-standard configurations)

Nice to Have (Not Required)

• Prior experience in Support Engineering, SRE, or Solutions QA roles
• Familiarity with AWS, Snowflake, Databricks, or enterprise data warehouse environments
• Scripting or automation skills (e.g. Python, Bash, Postman collections, etc.)
• Exposure to data quality tools or ETL pipeline validation
• Background in high-scale enterprise B2B SaaS environments Contract Structure
• Work Model: Embedded in support-product loop; close collaboration with Engineering and Product