Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading global communications and community platform has a highly engaged community of millions of daily active users who use the platform for many different reasons, with video gaming being a major part of the experience. The organization plays a uniquely important role in the future of gaming and is focused on making it easier and more enjoyable for people to connect before, during, and after playing games.
The organization is seeking a seasoned Senior Data Engineer, Ads, focusing on advertising product data. In this role, the successful candidate will drive technical vision and strategy for ads data engineering in support of ML initiatives while building and maintaining sophisticated data pipelines, datasets, and analytical tools. The role will lead cross-functional initiatives to transform advertising products through data-driven insights and mentor fellow engineers to deliver exceptional results.
What You’ll Be Doing
- Design and own core ads data models: fact/dim tables, canonical datasets, and aggregation layers that power delivery, measurement, targeting, attribution, and ML use cases.
- Build and maintain the ML data that enables ads ranking, delivery, and targeting – including feature development, label generation workflows, intra-day training, and ML input observability to catch data quality issues before they degrade model performance.
- Build conversion measurement pipelines and integrate third-party attribution data – including Conversion Attribution and Mobile Measurement Partner (MMP) integrations (Adjust, AppsFlyer, Singular) – ensuring attribution accuracy and data parity across measurement surfaces.
- Build batch and near real-time pipeline infrastructure across the ads ecosystem – pushing toward lower-latency data for ML and reporting use cases on the BigQuery + dbt + Dagster stack.
- Partner with Data Platform on launch and success of new data processing engines to support low latency requirements.
- Develop data quality frameworks, monitoring systems, automated anomaly detection, and SLA infrastructure for critical ads pipelines at massive scale.
- Proactively identify foundational data infrastructure gaps – including those with broad implications across ML, measurement, and reporting – and design scalable, canonical solutions that multiple teams can depend on.
- Build systems from scratch in a rapidly evolving, greenfield advertising data environment – making sound architectural decisions with incomplete information and balancing short-term delivery with long-term infrastructure investment.
- Drive alignment across Data Science, ML Engineering, Ads Product, and GTM teams through clear narratives that connect data infrastructure decisions to business outcomes and revenue impact.
- Mentor engineers through technical challenges, code and design reviews, and ownership of complex projects – contributing to the culture and engineering standards of the Data Engineering team.
What You Should Have
- 5+ years of hands-on experience writing production code and architecting data pipelines with high-volume consumer data in advertising technology domains (ad delivery, ranking, targeting, identity, conversion measurement).
- Deep expertise in digital advertising data engineering – specifically in ads delivery, conversion measurement, attribution pipelines, or ML feature data infrastructure.
- Experience with Conversion Data and APIs, MMP integrations, or identity graph infrastructure is strongly valued.
- Demonstrated experience building data models in a greenfield or 0-to-1 environment where requirements change frequently, documentation is sparse, and architectural decisions are made with incomplete information.
- Expert-level SQL and Python.
- Strong ability to design performant, maintainable data models and write production-quality pipeline code.
- Proven hands-on experience with data quality audits, monitoring systems, and automated anomaly detection for massive-scale datasets (billions+ rows) – including quality frameworks designed for ML inputs.
- Strong technical communication skills with the ability to drive alignment, influence prioritization, and earn adoption from technical stakeholders.
- Collaborative mindset and strong cross-functional instincts with experience building trusted working relationships with Data Science, ML Engineering, and Product teams.
Disclaimer
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- No Client Relationship: This company is not a client of SpringCube unless stated.
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