Overview
About Business Unit:
The Product team forms the crux of our pofwerful platforms and helps connect millions of customers worldwide with the brands that matter most to them. This team of innovative problem solvers develops and builds products that position Epsilon as a differentiator, encouraging an open and balanced marketplace built on respect for individuals, where every brand interaction holds value. Our full-cycle product engineering and data teams chart the future and set new benchmarks for our products, by using industry standard methodologies and sophisticated capabilities in data, machine learning, and artificial intelligence. Driven by a passion for delivering smart end-to-end solutions, this team plays a key role in Epsilon's success story.
What you will enjoy in this role:
The Epsilon Activation Delivery Platform team builds the core frameworks and connectors powering our audience activation business, processing and moving trillions of user events every month to some of the world's largest advertising and Connected TV platforms, including Meta, Google, Amazon, LG, and Samsung.
As our dedicated Quality & Reliability lead, you will define and drive the quality engineering and platform reliability strategy for the Activation Delivery Platform. You will architect scalable test automation and data-validation frameworks, establish CI/CD quality gates, and strengthen platform reliability through observability, SLOs, and disciplined incident management practices.
You will play a critical role in establishing and driving engineering quality standards across an engineering team distributed across the US and India. Working closely with engineering leadership, data engineers, product managers, and partner teams, you will ensure that quality, reliability, and operational excellence are embedded throughout the software development lifecycle.
Your work will directly contribute to delivering accurate, reliable, and resilient activation systems that meet our customers' expectations and support the scale and performance of our global business.
Reporting Structure & Day-to-Day Collaboration
This role reports to the Senior Manager of Engineering and collaborates closely with data engineers, product managers, engineering leadership, and partner teams across the US and India.
You will participate in architecture and design reviews, code reviews, release planning, and the end-to-end delivery of critical business capabilities. You will also work with broader People Cloud engineering teams to leverage and align with established engineering frameworks, platforms, processes, and reliability standards.
Why we are looking for you:
Operate at True Scale: Help ensure that pipelines processing and routing trillions of records every month remain accurate, observable, and resilient. The quality standards and reliability practices you establish will directly protect customer experiences and critical business outcomes.
Shape Quality on a Modern Data Stack: Architect and implement test automation, data-validation, and observability frameworks across a modern big data ecosystem using Spark, Scala, Python, AWS EMR, and Databricks. Work with distributed data systems, testing data outputs, APIs, and integrations at a significant scale.
Establish Engineering Quality Standards: Take ownership of defining and driving quality engineering practices for the Activation Delivery Platform, using and extending established frameworks, processes, and engineering standards across the broader People Cloud ecosystem.
See Your Impact: Directly influence the reliability and quality of integrations connecting our business to the world's largest technology, social media, and Connected TV platforms, including Meta, Google, Amazon, LG, and Samsung.
Elevate Engineering Excellence: Drive consistent adoption of quality engineering standards, automation practices, and reliability principles across a 12-person engineering team distributed across the US and India, enabling predictable releases, faster feedback cycles, and greater engineering confidence.
Pioneer AI-Assisted Quality Engineering: Leverage AI-assisted development tools such as Cursor to accelerate test creation, debugging, failure analysis, and incident triage. Explore and introduce agentic workflows that improve the efficiency and effectiveness of quality and reliability engineering.
Click here to view how Epsilon transforms marketing with 1 View, 1 Vision and 1 Voice.
Responsibilities
- Own the Test Automation Strategy
- Design and implement scalable test automation frameworks for Spark-based (Scala/Python) data pipelines and platform connectors.
- Establish comprehensive testing strategies covering unit, integration, contract, end-to-end, and regression testing.
- Integrate automated test suites into the development lifecycle to ensure consistent quality validation with every code change.
- Establish reusable testing patterns, engineering guidelines, and standard processes across the team.
- Validate Data Quality at Scale
- Design and implement data-validation and data-quality frameworks to ensure schema integrity, completeness, accuracy, consistency, and freshness.
- Build automated validation and reconciliation mechanisms for large-scale distributed datasets.
- Establish quality checks across data ingestion, transformation, processing, and outbound activation pipelines.
- Ensure data integrity and correctness before information is delivered to external partners such as Meta, Google, Amazon, LG, and Samsung.
- Integrate data-quality checks into both CI/CD workflows and production environments.
- Build Quality Gates into CI/CD
- Establish automated quality gates and release criteria across CI/CD pipelines.
- Define and enforce appropriate test coverage, code quality, security, and reliability thresholds.
- Implement automated regression testing and validation as part of continuous integration and deployment workflows.
- Promote safe deployment practices, early defect detection, and continuous feedback mechanisms.
- Align quality engineering practices with established People Cloud engineering standards and delivery processes.
- Drive Platform Reliability and Operational Excellence
- Define and implement Service Level Objectives (SLOs), Service Level Indicators (SLIs), and Service Level Agreements (SLAs) for critical pipelines and platform connectors.
- Establish comprehensive observability through metrics, structured logging, distributed tracing, alerting, and operational dashboards.
- Monitor and improve the reliability, availability, performance, and resilience of AWS EMR and Databricks workloads.
- Drive effective incident management, root cause analysis, and preventive actions to minimize recurring issues.
- Establish reliability practices that improve Mean Time to Detect (MTTD), Mean Time to Recover (MTTR), and overall platform stability.
- Drive Engineering Standards and Continuous Improvement
- Establish and champion quality engineering standards, development guidelines, and automation procedures across the engineering team.
- Collaborate with engineering leadership to integrate quality and reliability practices into architecture, design, development, and release processes.
- Find opportunities to improve engineering productivity, reduce technical debt, and strengthen platform resilience.
- Leverage existing People Cloud engineering frameworks, tools, and processes while finding opportunities for platform-specific improvements.
- Champion AI-Assisted Quality Engineering
- Leverage AI-assisted tools such as Cursor to accelerate test development, debugging, code analysis, and failure investigation.
- Explore AI driven approaches for automated test generation, defect prediction, and intelligent failure analysis.
- Evaluate and introduce agentic workflows that can improve test coverage, accelerate incident triage, and reduce manual engineering effort.
Qualifications
What You'll Bring with You
- 8-10 years of overall software engineering experience, with substantial experience in Quality Engineering, Test Automation, Data Quality, or Platform Reliability.
- Proven experience designing, architecting, and implementing scalable test automation frameworks, covering unit, integration, contract, and end-to-end testing.
- Strong programming skills in Python and/or Scala, along with fluent SQL skills for validating and reconciling large datasets.
- Hands-on experience testing distributed data pipelines, including data-quality and data-validation checks covering schema, completeness, accuracy, consistency, and freshness.
- Experience designing and implementing CI/CD pipelines with automated quality gates, coverage thresholds, and release standards.
- Working knowledge of Site Reliability Engineering (SRE) principles, including SLOs, SLIs, SLAs, observability, incident management, and root cause analysis.
- Practical experience with AWS cloud environments and exposure to distributed data processing frameworks such as Apache Spark on EMR or Databricks.
- Demonstrated ability to establish engineering standards, influence development practices, and drive adoption of quality and reliability practices across engineering teams.
- Strong analytical and problem-solving skills, with the ability to troubleshoot complex distributed systems and data-related issues.
- Excellent communication and collaboration skills, with the ability to engage partners, understand business requirements, and translate them into effective technical solutions.
Technical Skills
Must-have
- Strong programming proficiency in Python and/or Scala, with hands-on experience developing automation frameworks and engineering solutions.
- Strong SQL skills for large-scale data validation, reconciliation, and data-quality analysis.
- Hands-on experience with Apache Spark and distributed data processing environments.
- Experience designing and implementing test automation frameworks using PyTest, ScalaTest, JUnit, or equivalent frameworks.
- Strong understanding of unit, integration, contract, regression, and end-to-end testing methodologies.
- Experience building automated data-quality and validation frameworks covering schema integrity, completeness, accuracy, consistency, and freshness.
- Experience integrating automated testing and quality gates into CI/CD pipelines using Jenkins, Bamboo, GitHub Actions, GitLab CI/CD, or equivalent.
- Practical experience with AWS cloud services, particularly EMR, S3, and CloudWatch.
- Exposure to Databricks or equivalent distributed data processing platforms.
- Experience with REST API testing, integration testing, and contract validation.
- Working knowledge of SRE practices, including SLOs, SLIs, SLAs, incident management, and root cause analysis.
- Understanding observability practices, including metrics, logging, monitoring, alerting, and operational dashboards.
Good-to-have
- Experience with data-quality frameworks such as Great Expectations, Deequ, or equivalent solutions.
- Experience with Delta Lake, Unity Catalog, and advanced Spark optimization techniques.
- Familiarity with observability tools such as Prometheus, Grafana, ELK Stack, or OpenSearch.
- Experience with distributed tracing, chaos engineering, resilience testing, and automated recovery mechanisms.
- Exposure AI-assisted development tools such as Cursor or GitHub Copilot for test generation, debugging, and code analysis.
- Experience exploring agentic AI workflows for automated test execution, failure analysis, and incident triage.
- Experience in AdTech/MarTech, programmatic advertising, identity resolution, audience activation, or data onboarding.
- Experience building and maintaining integrations with external advertising, social media, or Connected TV platforms.
- Experience establishing engineering quality standards and influencing practices across globally distributed engineering teams.
Additional Information
Epsilon is a global data, technology and services company that powers the marketing and advertising ecosystem. For decades, we've provided marketers from the world's leading brands the data, technology and services they need to engage consumers with 1 View, 1 Vision and 1 Voice. 1 View of their universe of potential buyers. 1 Vision for engaging each individual. And 1 Voice to harmonize engagement across paid, owned and earned channels.
Epsilon's comprehensive portfolio of capabilities across our suite of digital media, messaging and loyalty solutions bridge the divide between marketing and advertising technology. We process 400+ billion consumer actions each day using advanced AI and hold many patents of proprietary technology, including real-time modeling languages and consumer privacy advancements. Thanks to the work of every employee, Epsilon has been consistently recognized as industry-leading by Forrester, Adweek and the MRC. Epsilon is a global company with more than 9,000 employees around the world.
Our pillars aren't just words. They're how we show up every day.
- People centricity: We focus on employee well-being in an environment where colleagues truly care about each other.
- Collaboration: We work together, support one another, and collectively achieve goals.
- Growth: There are endless opportunities for growth through learning, development and career advancement.
- Innovation: We drive progress through cutting-edge solutions and forward-thinking approaches.
- Flexibility: We've created a balance between work and personal life, and we encourage adaptability to solve problems creatively.
Our values guide us to create value for our clients, our people and consumers.
- Act with integrity
- Work together to win together
- Innovate with purpose
- Respect all voices
- Empower with accountability
These pillars and values are our foundation-shaping our culture, guiding our decisions, and uniting us in common purpose.
Epsilon is an Equal Opportunity Employer.
Epsilon is committed to promoting diversity, inclusion, and equal employment opportunities by using reasonable efforts to attract, recruit, engage and retain qualified individuals of all ethnicities and backgrounds, including, but not limited to, women, people of color, LGBTQ individuals, people with disabilities and any other underrepresented groups, traits or characteristics.
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