Data Engineer - Billing Platform

About the role

You will own the design, implementation, and reliability of the core data pipelines that power usage metering and billing for a global SaaS platform. This role requires you to build ETLs and complex data processing workflows that translate raw event streams into accurate billing datasets. You will partner closely with platform engineers and analytics teams to ensure data structures align with commercial models and product decisions. Your work will directly influence how AppsFlyer derives insights and drives revenue optimization through data. You are expected to mentor colleagues and contribute to architectural discussions that shape the future of the billing data ecosystem. The role demands a strong sense of ownership over data integrity, pipeline performance, and scalability in a fast-growing environment. You will be responsible for understanding business requirements and translating them into robust, production-grade data solutions.

Key facts

What you'll do

Develop end-to-end ETLs and data infrastructure features from data processing logic to database choice and implementation through to API development.

Improve the scalability and performance of existing data pipelines by profiling bottlenecks and refactoring for efficiency.

Delve into existing pipelines and business logic, target weaknesses and add functionality to support evolving billing requirements.

Mentor, advise, and work collaboratively within the Biz Data & Dev group, with other development teams, and with all other business teams of the company.

Continuously learn and follow new technologies both for personal growth and to ensure we are making the most informed technology choices possible.

Collaborate on software-oriented projects as needed, bridging data engineering expertise with backend development work in a cross-functional environment.

Design data models and schema strategies that support high-volume transactional and analytical workloads for billing use cases.

Implement monitoring, alerting, and logging mechanisms to ensure pipeline reliability and rapid troubleshooting of data issues.

Evaluate and integrate new tools, libraries, and frameworks that enhance the efficiency and maintainability of data workflows.

Participate in on-call rotations to support production data issues and contribute to incident response procedures.

Work with stakeholders to translate business metrics requirements into data pipelines that ensure accurate aggregation and reporting.

Ensure data quality through validation checks, testing strategies, and documentation that support long-term maintainability.

Contribute to the definition of data contracts and interfaces between upstream event sources and downstream billing consumers.

Support the deployment of data pipelines through infrastructure as code practices and CI/CD workflows in cloud environments.

Requirements

3-5 years of experience in data engineering in Big Data environments.

2+ years of experience with Spark and its ecosystem.

Experience with JVM stack, build tools, and environment, with a strong emphasis on Scala proficiency.

Experience with public cloud environments, with AWS being a significant advantage, including S3, Athena, and RDS.

Hands-on experience with Hadoop ecosystem components, SQL, and both relational and NoSQL databases.

High proficiency with Python and at least one JVM-based language such as Scala, Java, or Kotlin.

Proficiency with SQL and substantial experience with ETL design, data modeling, and performance optimization.

A positive attitude, willingness to take ownership, and the ability to work independently with minimal supervision.

A mindset of continuous learning, curiosity, and eagerness to experiment with new technologies while evaluating their practical impact.

Strong analytical thinking and the ability to understand complex business problems and convert them into data logic.

Excellent communication skills to collaborate effectively with cross-functional teams and explain technical concepts to non-technical stakeholders.

Ability to manage multiple priorities in a fast-paced environment while maintaining attention to detail and data accuracy.

Comfort working in a polyglot tech environment where Scala, Python, Go, and Node.js are used to solve diverse engineering challenges.

Nice to have

Hands-on experience with modern AI tools such as LLMs and code assistants, and a proactive approach to integrating AI-driven solutions into data engineering workflows.

Practical notes

This role is based in Herzliya.

The position involves working with extreme data growth scenarios that require solutions designed to scale 10X or more.

Candidates must be comfortable working with production-grade technologies in dynamic, high-growth settings.