AppsFlyer processes 150+ billion events across its platform, and our Web Attribution team is at the center of extending that power beyond mobile. We’re building the data infrastructure that tracks, attributes, and serves web user journeys - from first click to conversion - at massive scale across thousands of apps and billions of sessions.
We’re looking for a Data Engineer who wants to own the full pipeline: from raw event ingestion through distributed processing and into the serving layer that powers our clients’ dashboards and APIs. This isn’t internal tooling - you’ll be building the actual product surface that advertisers and marketers rely on to understand what’s working on the web.
As AI reshapes how data is processed, queried, and served, you’ll also have the opportunity to integrate AI and LLM-based capabilities into our data workflows - from intelligent data validation and anomaly detection to AI-powered analytics interfaces that make our platform smarter for clients.
What you’ll do
- Build & Own End-to-End Pipelines: Design, build, and optimize data pipelines that process billions of web events (sessions, in-app events, conversions) with strict freshness and accuracy SLAs. You’ll work across the entire attribution data stack - from ingestion and distributed processing through to the serving layer.
- Ensure Data Integrity: Own the correctness of complex attribution logic - deduplication, organic vs. non-organic classification, primary attribution resolution, and cross-platform user stitching across massive, partitioned datasets.
- Performance Engineering: Optimize scalable distributed processes to meet strict client SLAs. Tune jobs, queries, and pipeline scheduling for maximum throughput and minimum latency.
- Ship Client-Facing Features: Partner with Product, R&D, and client-facing teams to turn raw web signals into production-grade, queryable data products that advertisers interact with directly.
- Leverage AI in Data Workflows: Explore and integrate AI/ML capabilities into the data platform - from automated data-quality checks and anomaly detection to LLM-powered analytics tools that enhance how clients interact with their data.
What you have
- 3-5 years in Data / Big Data Engineering, with hands-on production experience at scale.
- Strong experience with distributed processing frameworks (e.g., Spark) - you understand execution plans, memory management, and how to debug performance bottlenecks.
- Solid SQL skills and experience with analytical data warehouses as both a processing and serving layer.
- Experience with workflow orchestration and cloud compute environments at production scale.
- Strong programming fundamentals - clean, testable, production-grade code. Scala preferred.
- A natural instinct for data quality: you think about edge cases, dedup logic, and “what happens when this field is n