Senior Data Platform Engineer

Airwallex (airwallex.com) is the leading financial technology platform for modern businesses growing beyond borders. With one of the world’s most powerful payments and banking infrastructures, our technology empowers businesses of all sizes to accept payments, move money globally, and simplify their financial operations, all in one single platform.

Established in 2015 in Melbourne, our purpose is to connect entrepreneurs, business builders, makers, and creators with opportunities in every corner of the world. Today, Airwallex has a global footprint across Asia-Pacific, Europe, and North America.

Who We Are? Team Introductions:

The Knowledge Platform team is at the heart of our company's data and AI strategy. We are building the foundational infrastructure that empowers the entire company to leverage data, AI, and ML into business impact. We accomplish this by creating platforms that handle the entire data and AI/ML lifecycle, simplifying the interface while providing proper safety and governance. This includes managing our data infrastructure (Databricks, Spark, Kafka, etc.), the technology to serve that data to our users (RAG, MCP, etc.), and the platform to host and govern these AI/ML models.

Role Overview:
The data platform engineering function is at the heart of our 2025 growth agenda. We are building a robust, scalable data infrastructure to support rapid experimentation, user-centric decision-making, and seamless growth execution. Key priorities include:

• Building a Customer Data Platform (CDP) to unify and validate user identities across touchpoints, creating a single source of truth for user behavior and attributes.

• Developing end-to-end data pipelines to power growth campaigns, user segmentation, and personalized experiences.

• Evolving our data architecture to support agile growth strategies, with a focus on real-time responsiveness and reliability.

What You’ll Do:

• Lead technical design, development, and optimization of data infrastructure for the Growth Team, spanning from foundational layers to application-specific components. This includes building the Customer Data Platform (CDP) to unify and confirm user identities, ensuring accurate and consistent user profiling across the business.

• Design and implement ETL workflows and data processing pipelines (batch and streaming) to ingest, transform, and deliver high-quality data for growth analytics, user segmentation, and campaign activation.

• Develop and automate growth-focused tools and a growth engine—enabling the team to run A/B tests, trigger personalized user journeys, and scale growth experiments efficiently.

• Collaborate with growth analysts, product managers, and engineers to translate business needs into data solutions, ensuring the platform supports rapid iteration and data-driven decision-making.

• Advocate for best practices in software engineering and data reliability, establishing scalable standards for data quality, pipeline monitoring, and infrastructure maintainability.

• Contribute to defining shared data tooling standards for the Growth Team, improving productivity and consistency across data engineering efforts.

• Mentor junior engineers, sharing expertise in distributed systems, data architecture, and growth-focused data engineering.
  

What We’re Looking For

• 5+ years of experience as a data engineer, with a track record of building scalable data platforms or pipelines, preferably in a growth, fintech, or consumer-focused environment.

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or related fields (or equivalent experience).

• Proficiency in programming languages such as Kotlin, Java, Scala, Python, or SQL. Hands-on experience with tools like Kafka, Spark, Flink, DBT, Hadoop, BigQuery, or Snowflake is strongly preferred.

• Experience building or working with Customer Data Platforms (CDPs) or user identity resolution systems is a plus.

• Strong expertise in ETL processes, batch/streaming data processing, and designing distributed systems that handle PB-scale data.

• Ability to own end-to-end data solutions—from architecture design to deployment, monitoring, and iteration.

• Familiarity with growth analytics, A/B testing frameworks, or marketing technology (MarTech) integrations is advantageous.