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.
- 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.
- 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.
- Experience building or maintaining attribution / AdTech data systems.
- Advanced Scala or functional programming experience.
- Experience with attribution models (last-click, multi-touch, view-through).
- Experience with SQL-based transformation frameworks (Dataform, dbt) or data-mesh / data-product platforms.
- Experience with Kafka or streaming systems.
- Experience running LLMs or ML pipelines in production.
- Being introduced by an AppsFlyer team member
As a global company operating from 25 offices across 19 countries, we reflect the human mosaic of the diverse and multicultural world in which we live. We ensure equal opportunities for all of our employees and promote the recruitment of diverse talents to our global teams without consideration of race, gender, culture, or sexual orientation. We value and encourage curiosity, diversity, and innovation from all our employees, customers, and partners.
“As a Customer Obsessed company, we must first be Employee Obsessed. We need to make sure that we provide the team with the tools and resources they need to go All-In.” Oren Kaniel, CEO
Skills Required
- 6+ years of experience in data or big data engineering, including production systems at scale
- Bachelor of Science in Computer Science or equivalent
- Deep hands-on Apache Spark experience, including execution plans, partitioning, memory management, debugging data skew, and performance optimization
- Deep experience with a cloud analytical warehouse such as BigQuery, Snowflake, Redshift, or Databricks SQL
- Experience designing data systems, writing design documents, making cost, latency, and correctness trade-offs, and taking designs to production
- Experience with workflow orchestration such as Airflow and cloud environments at production scale
- Strong programming fundamentals and ability to write clean, testable, production-grade code
- Scala preferred and Python for orchestration
- Strong focus on data quality, edge cases, deduplication, and null handling
- Hands-on use of AI tools in daily engineering work and understanding of their appropriate use in data engineering
- Experience building or maintaining attribution or AdTech data systems
- Advanced Scala or functional programming experience
- Experience with attribution models such as last-click, multi-touch, or view-through
- Experience with Dataform, dbt, data mesh, or data product platforms
- Experience with Kafka or streaming systems
- Experience running LLMs or machine learning pipelines in production
- Employee referral from an AppsFlyer team member
AppsFlyer Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about AppsFlyer and has not been reviewed or approved by AppsFlyer.
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Wellbeing & Lifestyle Benefits — Wellbeing support is positioned as a meaningful part of the rewards package, including private health insurance and wellness offerings like fitness classes. Additional extras such as events, work‑from‑home support, and global mobility opportunities are portrayed as adding tangible value beyond cash compensation.
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Equity Value & Accessibility — Equity is framed as broadly accessible, with stock options described as available to all employees. This expands the total rewards mix and can make overall compensation feel stronger even when salary alone is debated.
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Strong & Reliable Incentives — Total compensation in certain technical and revenue roles is presented as capable of reaching competitive levels, implying meaningful upside where role/market fit is strong. Sales compensation is described as potentially attractive on-target, which can reinforce perceived earning opportunity when performance aligns with plan design.
AppsFlyer Insights
What We Do
AppsFlyer helps brands make good choices for their business and their customers through innovative, privacy-preserving measurement, analytics, fraud protection, and engagement technologies. Built on the idea that brands can increase customer privacy while providing exceptional experiences, AppsFlyer empowers thousands of creators and 8,000+ technology partners to create better, more meaningful customer relationships. To learn more, visit www.appsflyer.com.
