Manager, Data Engineering
Engineering Manager · Manager · Full Time
Opens Airwallex's application page
Role
What you'll do.
Manager, Data Engineering at Airwallex leads a team of data engineers building foundational data infrastructure for a global payments platform serving 250,000+ businesses. This hybrid San Francisco role requires 8+ years of ETL pipeline design experience and 2+ years of team leadership, with expertise in data modeling, governance, and the emerging intersection of data and AI. You'll drive technical direction across multiple data domains while coaching engineers and scaling data foundations that power analytics, regulatory reporting, and real-time decision-making.
Responsibilities
- Team Leadership and Recruitment: Hire, onboard, and develop high-performing data engineering teams with clear expectations, regular feedback cycles, and structured career development pathways. Manage performance reviews, hiring plans, and workload allocation aligned with business growth trajectories and technical roadmaps.
- Technical Direction and Architecture: Establish and communicate technical strategy for data modeling across the organization, including schema design patterns (star schema, snowflake, normalized vs. denormalized architectures) tailored to specific business use cases and query patterns. Champion Single Source of Truth (SSOT) principles across all data layers.
- ETL Pipeline Engineering Oversight: Guide team design and implementation of batch and streaming ETL pipelines spanning ingestion, transformation, quality checks, and delivery. Collaborate with Data Platform Engineers and Product Managers on root-cause analysis, resolution of data quality issues, and scalable architectural solutions for distributed systems.
- Data Governance Strategy: Own data governance evolution including policies, standards, and best practices covering data quality, stewardship, metadata management, master data management, privacy/security, and data lifecycle. Represent data engineering interests in cross-functional governance decisions affecting compliance and data strategy.
- Stakeholder Collaboration and Communication: Partner with product managers, business stakeholders, and engineering leadership to translate ambiguous business requirements into well-structured, documented data solutions. Bridge technical and business perspectives while maintaining alignment on priorities and delivery timelines.
- Data and AI Integration: Drive strategic thinking on data-AI synergies, including building data foundations that support machine learning workflows, AI agents, retrieval-augmented generation (RAG), and other emerging AI/ML capabilities. Lead AI automation initiatives within the team.
- Code Review and Technical Mentorship: Conduct design reviews and code reviews for data engineering work, providing technical guidance in data modeling, pipeline engineering, and governance. Mentor engineers on distributed systems challenges including data migration, duplication, and consistency across multi-datacenter environments.
Qualifications
What we look for.
Technical
ETL Pipeline Design and Implementation
8+ years of hands-on experience designing and implementing ETL pipelines using enterprise integration platforms such as Informatica, Talend, Apache NiFi, or comparable data integration tools. Demonstrated expertise in building production-grade pipelines handling complex data transformations at scale.
SQL and Database Management
Advanced proficiency in SQL for complex query optimization, database design, and data analysis. Strong working knowledge of relational database management systems including MySQL, PostgreSQL, Oracle, and data warehouse solutions. Experience with query performance tuning and indexing strategies.
Cloud Data Platforms
Hands-on experience with Google Cloud Platform (GCP) specifically BigQuery for data warehousing and Airflow for orchestration. Understanding of cloud-native data architecture patterns, cost optimization, and managed services in GCP ecosystem.
Data Modeling and Architecture
Strong expertise in data modeling approaches including star schema, snowflake schema, dimensional modeling, and normalization techniques. Ability to evaluate schema design trade-offs based on analytical workloads, query patterns, and performance requirements. Experience establishing and enforcing data quality and consistency standards.
Data Governance and Metadata Management
Working knowledge of data governance frameworks, metadata management systems, data lineage tools, and regulatory compliance requirements. Experience implementing data cataloging, documentation standards, and stewardship practices in complex multi-domain environments.
Distributed Systems and Data Infrastructure
Understanding of distributed systems concepts, data consistency challenges, multi-datacenter architectures, and data replication patterns. Practical experience addressing challenges in data migration, synchronization, and eventual consistency across heterogeneous systems.
Education
Bachelor's Degree in Computer Science or Related Field
Bachelor's degree or higher in Computer Science, Information Systems, Finance, Mathematics, or equivalent technical discipline. Equivalent professional experience can substitute in lieu of formal degree for exceptionally qualified candidates.
Continuous Learning and Certifications
Preferred: Certifications in data management, cloud platforms, or related technologies. Active engagement with data engineering community through conferences, courses, or technical writing demonstrates commitment to staying current with evolving best practices.
Experience
Data Engineering Leadership
Minimum 2+ years of direct management experience leading data engineers, including hiring, performance evaluation, coaching, and professional development. Demonstrated success building high-performing teams and establishing team processes that balance delivery velocity with engineering rigor.
Building Scalable Data Platforms
Proven experience scaling data engineering infrastructure through company growth phases. Track record of designing systems that grow from startup metrics to enterprise-scale data volumes and user bases. Experience managing technical debt and platform evolution.
Cross-Functional Collaboration
Demonstrated ability to set technical direction and drive alignment with business stakeholders, product managers, and engineering leaders. Experience translating ambiguous business requirements into technical specifications and managing competing priorities in matrix environments.
Financial Technology or Payments Domain
Experience in fintech, payment systems, or financial services industries is highly valued. Understanding of regulatory requirements, compliance frameworks, and financial data domain specifics including transaction processing, reconciliation, and audit trails.
Skills
Required
SQL and Query Optimization
Expert-level SQL proficiency including complex joins, window functions, common table expressions, and performance optimization techniques. Ability to analyze query execution plans and optimize for both analytical and transactional workloads.
ETL Framework Expertise
Deep hands-on experience with enterprise ETL tools such as Informatica, Talend, Apache NiFi, or modern data pipeline orchestration platforms. Proficiency in designing fault-tolerant, idempotent pipelines with proper error handling and data quality checks.
Apache Spark and Distributed Processing
Working knowledge of Apache Spark for large-scale data processing, including RDDs, DataFrames, and optimization techniques for batch and streaming workloads. Experience with Databricks or comparable Spark-as-a-service platforms.
Data Warehouse Architecture
Understanding of modern data warehouse design patterns, particularly for cloud platforms like BigQuery. Knowledge of slowly changing dimensions, fact and dimension tables, data partitioning strategies, and materialized views for analytical query performance.
Problem-Solving and Systems Thinking
Excellent analytical and troubleshooting skills with strong attention to detail. Ability to break down complex data engineering challenges into tractable components, evaluate trade-offs, and implement robust solutions. Commitment to code quality and documentation.
Leadership and Communication
Proven ability to communicate technical concepts to both technical and non-technical audiences. Strong presentation skills for architecture reviews and stakeholder updates. Experience mentoring junior engineers and facilitating knowledge sharing within teams.
Kafka and Streaming Data
Experience with Apache Kafka or comparable streaming platforms for building real-time data pipelines. Understanding of stream processing patterns, exactly-once semantics, and designing systems for high-throughput, low-latency data delivery.
Preferred
Python and R for Data Analysis
Nice to haveScripting proficiency in Python or R for data analysis, pipeline automation, and building analytical tools. Experience using libraries like Pandas, NumPy, or dplyr for data transformation and exploratory analysis.
Retrieval-Augmented Generation (RAG) and AI/ML Pipelines
Nice to haveEmerging familiarity with building data foundations for AI/ML workloads, including feature engineering, training data preparation, and RAG pipeline infrastructure. Understanding of how data engineering supports generative AI applications at scale.
Financial Services Domain Knowledge
Nice to haveExperience in financial services, payment processing, or fintech sectors. Knowledge of concepts like transaction settlement, reconciliation, regulatory reporting (AML, KYC), and compliance requirements specific to financial institutions.
Data Cataloging and Lineage Tools
Nice to haveExperience with data catalog platforms such as Collibra, Alation, or Apache Atlas. Familiarity with data lineage tracking, data discovery interfaces, and building organizational metadata practices.
dbt and Modern Data Stack
Nice to haveHands-on experience with data build tool (dbt) for transformations, data documentation, and testing. Familiarity with modern data stack approaches including cloud data warehouses, reverse ETL, and analytics-engineering patterns.
Change Data Capture and CDC
Nice to haveExperience implementing Change Data Capture (CDC) patterns using Debezium, AWS DMS, or similar tools. Understanding of log-based replication and how CDC enables real-time data synchronization across systems.
DevOps and Infrastructure
Nice to haveFamiliarity with containerization (Docker), orchestration (Kubernetes), infrastructure-as-code tools, and CI/CD pipelines. Experience operating data pipelines in production, monitoring, alerting, and incident response.
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 200,000 – 335,000
Equity·Stock options
Full posting
Original listing.
About Airwallex
Airwallex is the only unified payments and financial platform for global businesses. Powered by our unique combination of proprietary infrastructure and software, we empower over 250,000 businesses worldwide – including Brex, Navan, Qantas, SHEIN and many more – with fully integrated solutions to manage everything from business accounts, payments, spend management and treasury, to embedded finance at a global scale.
Proudly founded in Melbourne, we have a team of over 2,300 of the brightest and most innovative people in tech across 27 offices around the globe. Valued at US$11 billion and backed by world-leading investors including T. Rowe Price, Visa, Mastercard, Robinhood Ventures, Sequoia, Salesforce Ventures, DST Global, and Lone Pine Capital, Airwallex is leading the charge in building the global payments and financial platform of the future. If you’re ready to do the most ambitious work of your career, join us.
Attributes We Value
We hire successful builders with founder-like energy who want real impact, accelerated learning, and true ownership. You bring strong role-related expertise and sharp thinking, and you’re motivated by our mission and operating principles. You move fast with good judgment, dig deep with curiosity, and make decisions from first principles, balancing speed and rigor.
You're humble and collaborative; turn zero‑to‑one ideas into real products, and you “get stuff done” end-to-end. You use AI to work smarter and solve problems faster. Here, you’ll tackle complex, high‑visibility problems with exceptional teammates and grow your career as we build the future of global banking. If that sounds like you, let’s build what’s next.
About the team
The Data & AI org 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.
In 2026, our team’s overarching mission is to evolve our full data ecosystem—encompassing both platform and models—into a fully AI agent-ready infrastructure; we will empower customers to engage directly with the data platform to extract actionable value through capabilities like analytics and natural language querying, while also upgrading the platform to deliver robust, real-time performance for instant, data-driven decision-making.
What you'll do
We’re looking for a Data Engineering Manager to lead a team within our Strategic Data Org and help scale the data foundations that power Airwallex’s products, analytics, and operational decision-making. In this role, you will lead engineers working on data modeling, pipelines, and analytics-ready datasets across domains such as regulatory reporting, data content foundation, customer and business data, and growth data. You’ll partner closely with engineering leaders, product and business stakeholders, and adjacent platform teams to turn ambiguous business needs into reliable, well-structured data solutions.
This is a hybrid role based in San Francisco.
Team Leadership & People Management
Hire, coach, and grow a team of data engineers, setting clear expectations and providing regular feedback and career development support.
Establish team rituals, priorities, and ways of working that balance delivery speed with engineering rigor.
Act as a technical mentor, reviewing designs and code where needed, and helping engineers grow their skills in data modeling, pipeline engineering, and governance.
Manage performance, workload, and hiring plans in line with business needs.
Drive AI strategy and AI automation for the team.
Data Modeling Strategy
Set the technical direction for data modeling across the team, ensuring the org selects appropriate schema designs (e.g., star schema, snowflake, normalized vs. denormalized) based on business use cases.
Champion the concept of Single Source of Truth (SSOT) across data layers and pipelines, and hold the team accountable to it.
Ensure your team collaborates effectively with business stakeholders to translate data needs into clean, structured, well-documented models.
Oversee data consistency, traceability, and quality standards across multiple data sources and domains.
ETL & Data Pipeline Oversight
Guide the team's approach to building and maintaining batch and streaming ETL pipelines, from ingestion through transformation and delivery.
Ensure strong collaboration between your team, Data Platform Engineers (DPEs), and Product Managers (PMs) to drive quick root-cause resolution of data issues and durable, scalable fixes.
Bring judgment to challenges around distributed or multi-datacenter systems, including data migration, duplication, and consistency, and help the team navigate them.
Data Governance
Own and evolve data governance strategy, policies, and standards for the team's domains.
Ensure the team's practices reflect the key pillars of data governance (data quality, data stewardship, metadata management, master data management, data privacy/security, data lifecycle).
Represent the data engineering team in cross-functional governance conversations and decisions.
Data + AI
Drive thinking on how data engineering and AI can work together in practical, high-impact ways, and help the team build the foundations that make that possible.
These areas — data modeling, ETL/pipelines, governance, and data + AI — are the core focuses of the DE team. You should have strong, credible expertise in at least one (ideally data modeling or ETL) with working knowledge across the others.
Who You Are
Minimum Qualifications:
Bachelor's degree or higher in Computer Science, Information Systems, Finance, Mathematics, or a related field.
8+ years of experience designing and implementing ETL pipelines using tools such as Informatica, Talend, Apache NiFi, or similar data integration platforms, including significant hands-on technical depth.
2+ years of experience directly managing or leading data engineers, including hiring, coaching, and performance management.
Proficiency in SQL, database management systems (e.g., MySQL, PostgreSQL, Oracle), and data warehousing solutions.
Familiarity with Google Cloud Platform (GCP), specifically BigQuery and Airflow.
Demonstrated ability to set technical direction and drive alignment across engineering and business stakeholders.
Excellent problem-solving skills, with a keen attention to detail and a commitment to producing high-quality work.
Strong communication and collaboration skills, with the ability to lead effectively in a fast-paced, team-oriented environment and work with globally distributed teams.
Preferred Qualifications:
Experience with financial industries, payment systems, or fintech platforms.
Knowledge of data governance practices and regulatory requirements in the financial industry.
Experience with scripting languages (e.g., Python, R) for data analysis and automation.
Certification in data management or related technologies.
Prior experience scaling a data engineering team through periods of significant company growth.
Applicant Safety Policy: Fraud and Third-Party Recruiters
To protect you from recruitment scams, please be aware that Airwallex will not ask for bank details, sensitive ID numbers (i.e. passport), or any form of payment during the application or interview process. All official communication will come from an @airwallex.com email address. Please apply only through careers.airwallex.com or our official LinkedIn page.
Airwallex does not accept unsolicited resumes from search firms/recruiters. Airwallex will not pay any fees to search firms/recruiters if a candidate is submitted by a search firm/recruiter unless an agreement has been entered into with respect to specific open position(s). Search firms/recruiters submitting resumes to Airwallex on an unsolicited basis shall be deemed to accept this condition, regardless of any other provision to the contrary.
Equal opportunity
Airwallex is proud to be an equal opportunity employer. We value diversity and anyone seeking employment at Airwallex is considered based on merit, qualifications, competence and talent. We don’t regard color, religion, race, national origin, sexual orientation, ancestry, citizenship, sex, marital or family status, disability, gender, or any other legally protected status when making our hiring decisions. If you have a disability or special need that requires accommodation, please let us know.
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