Senior Data Engineer

About the role

AppsFlyer processes 150+ billion events across its platform, and our Web Attribution team is at the center of extending that power beyond mobile. We build the product that shows advertisers which campaigns really drive results on the web. We attribute every web user journey, from first click to conversion, across thousands of apps and billions of sessions.

We're looking for a Senior Data Engineer to own the technical direction of our pipelines. You'll be the team's go-to data engineering expert and a key knowledge holder: the person the team turns to for Spark, data modeling, and the hardest pipeline problems. You'll lead the team's design work and work closely with the team lead on planning and technical priorities. You'll shape the product that advertisers and marketers rely on every day.

What you’ll do

- Build & Own End-to-End Pipelines: Build and improve pipelines that process billions of web events (sessions, in-app events, conversions) with strict freshness and accuracy SLAs, from ingestion through distributed processing to the serving layer.

- Lead Design & Architecture: Own the technical design of the team's pipelines, from spec to production. Write design docs, review the team's designs, and make the key trade-offs, such as what runs in SQL and what runs in Spark. Work with the group's architects on cross-team designs.

- Drive Technical Planning: Work with the team lead on the roadmap, planning, and priorities. Break large projects into clear deliverables. Represent the team in technical discussions with Product, R&D, and partner teams. Own production incidents end-to-end and turn them into lasting fixes.

What you have

- 6+ years in Data / Big Data Engineering, including building and running production systems at scale.

- B.Sc. in Computer Science or equivalent.

- Deep, hands-on Apache Spark experience: you understand execution plans, partitioning, and memory management, and you know how to debug skew and performance problems at scale.

- Deep experience with a cloud analytical warehouse (BigQuery, Snowflake, Redshift, or Databricks SQL) as both a processing and serving layer. BigQuery is a strong advantage.

- A proven track record in data-system design: you've written design docs, made trade-offs between cost, latency, and correctness, and taken designs all the way to production.

- Experience with workflow orchestration (e.g., Airflow) and cloud environments at production scale. GCP preferred.

- Strong programming fundamentals: clean, testable, production-grade code. Scala preferred; Python for orchestration.

- A natural instinct for data quality: you think about edge cases, dedup logic, and "what happens when this field is null" before anyone asks.

- Hands-on use of AI tools in your daily engineering work, and a clear view of where AI helps data engineering and where it doesn't.

Bonus points

- Experience building or maintaining attribution / AdTech data systems.