Job VC

About AppsFlyer:
AppsFlyer provides a SaaS marketing analytics platform that measures and optimizes marketing activities across mobile, web, CTV, PC, and console applications. AppsFlyer is known for its massive backend production. At any given moment, thousands of servers are consuming 150+ billion mobile app events, crunching our users’ data, serving requests, and communicating on a massive scale.

To maintain the universe we call AppsFlyer, we practice modern production operations with a complete self-serve CI/CD platform, a highly integrated observability stack for our micro-services, backends, and infrastructure, a culture of ownership and eagerness for quality.

The Analytics group is responsible for showing our clients the stories their data is telling. Through complex aggregation, mission-tailored analytical databases, and carefully crafted APIs we are able to provide slice-and-dice analysis in our beautiful dashboards and through the use of external integrations.

About the Role:
As a
Data Engineer
within the Analytics group at AppsFlyer, you will own the end-to-end journey of event data - from ingestion and processing to storage, modeling, and API delivery.

You will design and implement data pipelines using
Apache Airflow
and
Spark
, choose and optimize analytical databases, and integrate new components in our hybrid microservices environment. You will collaborate with product managers and cross-functional teams, troubleshoot production incidents, and influence our migration toward
Google Cloud
and
BigQuery
. This role offers autonomy over feature design, real impact on a platform handling billions of events daily, and the chance to contribute to open-source projects or speak at industry forums.

Key Responsibilities:
Develop and maintain data processing pipelines using Apache Airflow, Scala Spark or PySpark.
Select, model and optimize analytical databases for high-throughput, low-latency queries.
Design and implement backend services and APIs for data ingestion, aggregation and client consumption.
Monitor system performance and stability; diagnose and resolve production issues.
Collaborate with product managers and engineers to plan and deliver complex features.
Drive migration efforts to Google Cloud Platform, BigQuery and modern data tooling.
Participate in on-call rotations, alerting via PagerDuty and incident response procedures.
Document system architecture and data flows; maintain code quality and tests.

Required Competence and Skills:
3+ years of software
development
experience with a
focus on data engineering
.
Practical experience with
Apache Spark
(Scala or PySpark) and
Apache Airflow
.
Experience building and maintaining high-throughput, low-latency distributed data systems.
Strong SQL
skills and experience with data modeling for analytics workloads.
Familiarity with
JVM languages
(
Scala, Java, Clojure
) or
Python
for backend development.
Experience with cloud platforms - preferably
GCP
- and data warehouses such as
BigQuery
.
Proven ownership of production systems and
on-call incident
management.
Excellent
communication skills in
English
for distributed teamwork.
B.Sc. in Computer Science or equivalent practical experience.

Nice to Have:
Production experience with large-scale databases and ETL tooling.
Hands-on background in Go or Clojure backend services.
Experience with production-grade BigQuery ETL and data pipelines.
Contribution to open-source projects or public speaking at meetups/conferences.
An “AI mindset” - familiarity with tools like GitHub Copilot and emerging AI workflows.

Benefits Package includes:
Health Insurance
Paid Unlimited Vacation Days + all national holidays + additional recharge days
Meals Reimbursement
Sport Reimbursement
Breakfast in the office
Mental health program
Team building, happy hours, and other team activities
Paid sick days
Snacks, fruits & ice-cold beer
All new team members are provided with a brand-new Mac laptop with 2 monitors and a Starter package