Senior Quality Assurance Engineer, Data & Platform Engineering

lgads; Denver (NY); On-site; USD 100,000 - 165,000; Full time

Benefits offered by this job

100% employer-paid medical, dental, &视

401(k) with company match

Flexible Time Off

Paid parental leave

On-site perquisites in US offices

Job summary

LG Ad Solutions is seeking a Senior QA Engineer to lead quality within our Data & Platform Engineering team, owning terabyte-scale data pipelines and governance. You will design strategies, build automation for Spark and Airflow, and ensure data quality across CI/CD.

This role spans collaboration with data engineers and platform engineers to elevate testing at scale. This on-site position requires 4 days/week in our Denver or NYC offices, with no relocation support and a strong focus on data

Qualifications

• 7+ years of QA engineering experience.
• Experience testing data pipelines with Databricks, Spark and Airflow.
• Hands-on experience building extensible test automation frameworks.
• Knowledge of ETL/ELT, data quality dimensions and schema validation.
• Ability to define quality metrics, simplify testing and remove bottlenecks.
• Experience establishing quality gates in CI/CD pipelines.
• Strong communication of testing strategy and risks to stakeholders.

Responsibilities

• Design and lead test strategies for data pipelines and platform quality.
• Build scalable test automation for Spark, Airflow, and backend services.
• Own data quality gates across CI/CD and ensure schema validation.
• Define the quality bar with engineers and hiring manager for new features.
• Create instrumentation and metrics to measure quality pre-release and in production.
• Identify architectural deficiencies affecting data quality and drive improvements.
• Coordinate testing across a distributed US-India team.
• Mentor engineers on data mocking, test data management, and idempotency testing.
• Influence engineering decisions to improve product quality and reduce defects.

Skills

Databricks

Spark

Airflow

Test automation

CI/CD

Python

Data pipelines

Data quality

Education

Bachelor’s or Master’s in CS/Engineering

Tools

Jenkins

GitHub Actions

Job description

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

• 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

Nice to Have

• 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 [e-mail] to let us know the nature of your request.

Application Deadline: September 30, 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.