GroundTruth is an advertising platform that turns real-world behavior into marketing that drives in-store visits and other real business results. We use observed real-world consumer behavior, including location and purchase data, to create targeted advertising campaigns across all screens, measure how consumers respond, and uncover unique insights to help optimize ongoing and future marketing efforts.
With this focus on media, measurement, and insights, we provide marketers with tools to deliver media campaigns that drive measurable impact, such as in-store visits, sales, and more.
Learn more at groundtruth.com.
We believe that innovative technology starts with the best talent and have been ranked one of Ad Age’s Best Places to Work in 2021, 2022, 2023 & 2025! Learn more about the perks of joining our team here.
About Us
GroundTruth is looking for a Data Engineering Manager with strong expertise in designing and building scalable data platforms and pipelines to join our team. The Data Engineering Team is responsible for the core data infrastructure that powers our audience platform.
As an Engineering Manager on our Audience Engineering team, you will build solutions that add new data capabilities and analytical depth to our platform while managing sophisticated AWS-native data services.
You will:
- Architect Scalable Pipelines: Oversee the design and deployment of large-scale distributed data processing jobs using PySpark on Amazon EMR clusters and serverless AWS Glue ETL jobs.
- Coach and mentor engineers—supporting growth in technical skills (particularly Python and Spark optimization), data modeling best practices, and career progression.
- Partner with stakeholders and engineering leadership to evaluate, plan, and deliver data-first projects across advertising systems, analytics services, and reporting features.
- Lead by example: Write production-ready Python and PySpark code, perform code reviews, and optimize Spark configurations to improve performance and reduce costs. Apply Agile methodologies such as Scrum to drive iterative development, foster team collaboration, and ensure continuous delivery of high-quality data solutions.
- Support engineers through regular 1:1s, feedback, quarterly reviews, recognition, and performance management.
You have:
- Bachelor’s degree in Computer Engineering, Data Science, or equivalent practical experience.
- 8+ years of experience in technology, specifically focused on data engineering, data warehousing, or big data architecture.
- 2+ years of experience of leading a data engineering team.
- Expertise in Python & PySpark: Deep experience writing and tuning distributed processing applications, handling data skew, and optimizing Spark memory management.
- Advanced AWS Expertise: Proven track record of managing Amazon EMR for heavy-duty processing.
- Experience with Big Data Infrastructure: Build the systems required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS ‘big data’ technologies (S3, EMR, Glue, Athena and Lambda).
- Expert SQL skills for complex transformations, performance tuning, and deep-dive analytics.
- Experience with Orchestration: Advanced proficiency with Airflow and Git.
- AI-Driven Engineering: Proven track record of leveraging AI across the data engineering process to drive modernization, automate data quality checks, and enhance delivery outcomes.
- Hands-on familiarity with AI-native tools such as Cursor, Claude, or GitHub Copilot to scale data development.
How you can impress us:
- Performance Tuning Specialist: Ability to debug complex PySpark and/or Scala jobs and optimize EMR Instance Fleets/Spot Instances to balance performance with infrastructure costs.
- Good to have experience with event-driven architecture and hands-on experience using AWS SQS for scalable, reliable event processing.
- AWS certification is preferred, demonstrating expertise in designing and building scalable cloud-based data solutions.
- Organized and collaborative—comfortable in a fast-moving, data-intensive environment.
- Detail-oriented: Catches data quality issues early and implements automated course-corrections.
- Strong communicator who aligns business needs with technical data constraints through clear trade-offs.
- Deep problem solver who diagnoses pipeline bottlenecks and partners across teams to drive durable data solutions
