Senior Quality Assurance Engineer, Data & Platform Engineering
lgads · Denver, CO ·
- Seniority
- Senior
- Employment
- Full time
- Category
- Devops
- Experience
- 7+ years
lgads · Denver, CO ·
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.
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.
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
Experience testing data pipelines. This is a must-have requirement. Your experience should show proficiency in Databricks, Spark, Airflow or similar technologies.
7+ years of QA engineering experience
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
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
LG Ad Solutions provides equal work opportunities to all team members and applicants, and it prohibits discrimination and harassment of any type on the basis of race, color, ethnicity, caste, religion, age, sex (including pregnancy), national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by our policies or federal, state, or local laws.
We want to ensure that our hiring process is accessible. If you need reasonable accommodation for any part of the application process because of a medical condition or disability, please send an email to [email protected] to let us know the nature of your request.
Application Deadline: December 31, 2026
Compensation & Benefits
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/yr
Offers Bonus
Benefits & Perks: 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.
*Certain markets, including the San Francisco Bay Area, New York City Tri-State Area, Los Angeles Metro Area, and Seattle Metro Area, are aligned to Tier 1 compensation bands. All other U.S. locations are aligned to Tier 2 compensation bands. 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.