Senior Quality Assurance Engineer, Data & Platform Engineering

We require people to be on-site, 4 days/week at our Denver or NYC office and are unable to offer relocation support.

About the role

LG Ad Solutions is a global leader in connected TV (CTV) and cross-screen advertising. We pride ourselves on delivering state-of-the-art advertising solutions that integrate seamlessly with today's ever-evolving digital media landscape.

The Senior QA Engineer will be the quality leader embedded directly within our Data & Platform Engineering team. This team builds and owns terabyte-scale data pipelines, platform tooling, and data governance frameworks that sit at the core of our advertising technology. 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. This is not a generic QA role. 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)
• Mentor engineers on testing best practices specific to data systems—data mocking, test data management, pipeline idempotency testing, and more
• Influence engineering decisions across team boundaries to continuously improve product quality and reduce defect escape rates

Requirements

• 7+ years of QA engineering experience, with meaningful time spent testing data pipelines, backend services, or distributed systems
• 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
• Working knowledge of data engineering concepts: ETL/ELT patterns, pipeline orchestration, data quality dimensions (completeness, consistency, timeliness), schema validation
• Demonstrated ability to define and implement quality metrics, simplify testing processes, and remove bottlenecks
• Experience establishing quality gates in CI/CD pipelines (Jenkins, GitHub Actions, or similar)
• Strong judgment on technical trade-offs between short-term needs and long-term quality architecture
• Clear communicator who can convey testing strategy and quality risks to both technical and non-technical stakeholders
• 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

Pay

Compensation varies by location:

• Tier 1 (San Francisco Bay Area, New York City Tri-State Area, Los Angeles Metro Area, and Seattle Metro Area): $115,000 - $165,000/year
• Tier 2 (all other U.S. Locations outside of Tier 1): $100,000 – $143,000/year

Offers may include a bonus. The compensation offered will take into account internal equity and may vary depending on the candidate's geographic region, job-related knowledge, skills, and experience.

Benefits

• 100% employer-paid medical, dental, and vision coverage for employees and eligible dependents
• Company-paid life and AD&D, STD and LTD insurance, plus optional supplemental coverage
• 401(k) with company match
• Flexible Time Off (FTO)
• Paid parental leave