Job Opportunities API

We require people to be on-site, 4 days/week at our Denver or NYC office and are unable to offer relocation support. 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 OPPORTUNITY We are looking for a Senior QA Engineer to 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. WHAT YOU’LL DO - 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 WHAT YOU BRING - 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, GC