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Senior Data Engineer

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Work setting
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Employment
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What you’ll work on

Full posting
  • Build and improve pipelines processing billions of web events under freshness and accuracy SLAs

  • Own technical design from spec to production, including design docs and design reviews

  • Decide trade-offs such as what runs in SQL versus Spark

  • Help plan the roadmap and break large projects into deliverables with the team lead

  • Own production incidents end-to-end and turn them into lasting fixes

From the employer’s posting
Build and improve pipelines that process billions of web events (sessions, in-app events, conversions) with strict freshness and accuracy SLAs
Own the technical design of the team's pipelines, from spec to production. Write design docs, review the team's designs
make the key trade-offs, such as what runs in SQL and what runs in Spark
Work with the team lead on the roadmap, planning, and priorities. Break large projects into clear deliverables
Own production incidents end-to-end and turn them into lasting fixes
Apply at AppsFlyer

Posting record

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Status in our records
Live
First seen by us
Oct 6, 202629 days ago
Recorded sightings
31
Last seen by us
Nov 4, 2026
Employer says posted
Oct 1, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 29

Similar jobs usually come down between day 5 and day 54.

From 10,736 similar postings that came down. Coming down is not always a hire.

AppsFlyer is hiring

3 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Hiring is picking up
  • 33% of open jobs were reposted
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • Scala
  • SQL
  • BigQuery
  • Databricks
  • dbt
  • Kafka
  • Redshift
  • Snowflake
  • Spark
  • Airflow
Where the posting names them
- 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.
- Experience building or maintaining attribution / AdTech data systems. - Advanced Scala or functional programming experience. - Experience with attribution models (last-click, multi-touch, view-through).
- 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.
- 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 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 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.
- 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.

Your next step

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About the role

We file this job under Data Engineer, by the work the posting describes.

Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.

Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.

An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.

US government figures for database architects

Median yearly pay, May 2025
$139,500
Expected change, 2025 to 2035
9.4% more jobsagainst 3.5% for all jobs

The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.

Job description

View original posting ↗

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.
  • 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

 

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