Lgads
Senior Quality Assurance Engineer, Data & Platform Engineering
Denver, CO · Senior
No sponsorshipDetected 131 days ago
PythonScalaDistributed SystemsBackend DevelopmentGitDatabricksAWSGCPAzureCloud PlatformsCI/CDGitHub ActionsJenkinsPlatform EngineeringSparkAirflowdbtData EngineeringSignal ProcessingTest AutomationMentoring
About the role
- We are looking for a Senior QA Engineer to be the quality leader embedded directly within our Data & Platform Engineering team.
- You will need to speak the language of data engineering-Apache Airflow, Spark, Databricks, cloud infrastructure-and bring a testing mindset that addresses the unique challenges of high-volume, high-velocity data systems.
- If you thrive on ambiguity, care deeply about quality at scale, and want your work to directly impact advertising revenue, this role is for you.
Responsibilities
- Design and lead comprehensive test strategies for complex, ambiguous data pipeline and platform quality challenges, including ETL validation, data quality checks, and pipeline observability
- Build scalable, maintainable test automation frameworks tailored to distributed data systems-covering unit, integration, and end-to-end testing of Spark jobs, Airflow DAGs, and backend services
- Establish and own data quality gates within CI/CD pipelines, ensuring schema validation, data completeness, and consistency checks are embedded throughout the development lifecycle
- Partner closely with Data Engineers, Platform Engineers, and the hiring manager to define the quality bar for new features and infrastructure changes
- Create instrumentation and metrics to measure quality both pre-release and in production, including anomaly detection and alerting across our data ecosystem
- Proactively identify architectural deficiencies affecting data quality and lead initiatives to address them
- Drive parallelized test plan design to enable independent execution across a globally distributed team (US and India)
- Proven ability to design and execute test plans for complex, ambiguous problem areas with limited guidance
- Hands-on experience building extensible test automation frameworks from scratch, not just maintaining existing ones
- You will work shoulder-to-shoulder with data engineers, understand the complexity of distributed systems and large-scale ETL workflows, and own quality from design through production.
Requirements
- 7+ years of QA engineering experience, with meaningful time spent testing data pipelines, backend services, or distributed systems
- Working knowledge of data engineering concepts: ETL/ELT patterns, pipeline orchestration, data quality dimensions (completeness, consistency, timeliness), schema validation
- Experience establishing quality gates in CI/CD pipelines (Jenkins, GitHub Actions, or similar)
- Experience mentoring engineers and improving overall team testing capabilities
Nice to have
- Familiarity with Apache Airflow, Apache Spark (PySpark or Scala), or Databricks
- Experience testing AdTech systems (DSP, SSP, ACR, or audience data platforms)
- Knowledge of cloud infrastructure testing on AWS, GCP, or Azure
- Experience with data observability tools (Great Expectations, Monte Carlo, dbt tests, or similar)
- Understanding of distributed systems concepts and how they impact testability
- Experience with service virtualization, mock services, or chaos/resilience testing
- Bachelor's or Master's degree in Computer Science, Engineering, or related field
- Proficiency in Python or another scripting language for test tooling
Skills
- LG Ad Solutions is a global leader in connected TV (CTV) and cross-screen advertising.
This listing is sourced directly from Lgads's careers page and normalized into a canonical job model.