Data Engineering Manager

Airwallex2 days ago

Location

US - San Francisco

Type

Full Time

Salary

USD 200,000 – 335,000

Level

Manager

Role

Engineering Manager

Posted

Jul 17, 2026

Full TimeManager

The role

Summary

Lead a data engineering team at Airwallex, the unified payments and financial platform serving 250,000+ global businesses. Drive data modeling strategy, ETL pipeline development, and data governance across regulatory reporting, analytics, and business intelligence domains while managing team hiring, coaching, and career development in a fast-paced fintech environment.

What you'll do

Team Leadership and People Management: Hire, onboard, and develop a high-performing data engineering team, establishing clear technical expectations, conducting regular performance reviews, and creating individual career development plans. Establish team rituals, coding standards, and collaborative workflows that balance delivery velocity with engineering rigor. Mentor engineers on advanced data modeling techniques, pipeline architecture, and governance best practices while fostering a culture of continuous learning and technical excellence.

Data Modeling and Schema Design Strategy: Define and enforce data modeling standards across the team, guiding selection of appropriate schema designs including star schema, snowflake, and normalized/denormalized approaches based on specific business use cases. Champion Single Source of Truth (SSOT) principles across all data layers and ensure teams maintain strict accountability to these standards. Partner with stakeholders to translate business requirements into clean, well-documented data models with clear lineage and metadata.

ETL Pipeline Architecture and Optimization: Lead the design and implementation strategy for batch and streaming ETL pipelines spanning data ingestion, transformation, and delivery stages. Collaborate with Data Platform Engineers and Product Managers to rapidly diagnose and resolve data incidents while implementing durable, scalable solutions. Navigate complex distributed system challenges including multi-datacenter deployments, data migration strategies, and consistency management across federated sources.

Data Governance and Quality Assurance: Own the evolution of data governance strategy, policies, and standards across assigned domains including regulatory reporting, business intelligence, and customer data platforms. Implement and enforce governance pillars spanning data quality, stewardship, metadata management, master data management, privacy/security compliance, and data lifecycle management. Represent the data engineering organization in cross-functional governance decisions and regulatory compliance initiatives.

Data and AI Integration: Drive strategic thinking on practical, high-impact convergence of data engineering and AI capabilities within the organization. Build foundational data infrastructure supporting AI/ML model development, RAG systems, and agent-ready platforms. Guide the team on AI automation opportunities that enhance productivity and enable customers to extract actionable insights through natural language querying and advanced analytics.

Technical Direction and Stakeholder Alignment: Set clear technical direction for data engineering initiatives across multiple domains and drive alignment across engineering leaders, product management, and business stakeholders. Make principled architectural decisions balancing speed and rigor while maintaining system reliability and data quality standards. Communicate complex technical concepts to non-technical audiences and translate ambiguous business requirements into concrete engineering roadmaps.

What we look for

Technical

ETL Pipeline Development8+ years of hands-on experience designing, building, and optimizing ETL pipelines using enterprise data integration platforms such as Informatica, Talend, Apache NiFi, or equivalent. Demonstrated expertise in managing full data pipeline lifecycle from source system ingestion through data transformation and analytics delivery.

SQL and Database ManagementAdvanced SQL proficiency including query optimization, complex joins, window functions, and performance tuning. Proven experience managing relational database management systems such as MySQL, PostgreSQL, or Oracle with deep understanding of indexing strategies, transaction management, and query execution plans.

Cloud Data WarehousingHands-on experience with Google Cloud Platform (GCP) specifically BigQuery and Apache Airflow. Understanding of cloud-native data architecture, cost optimization, and scalable analytics infrastructure. Experience with distributed computing frameworks and cloud data warehousing best practices.

Data Modeling and Schema DesignStrong expertise in data modeling methodologies including dimensional modeling, entity-relationship design, and schema optimization. Proficiency in designing scalable data structures for analytics including star schema, snowflake schema, and normalized/denormalized approaches based on use case requirements.

Data Governance and ComplianceWorking knowledge of data governance frameworks, metadata management, data quality frameworks, and regulatory compliance requirements. Understanding of data lineage tracking, data stewardship roles, and privacy-preserving data handling practices.

Distributed Systems and Data ArchitectureExperience navigating complex distributed systems including multi-datacenter architectures, data replication, eventual consistency patterns, and distributed transaction management. Knowledge of stream processing frameworks like Kafka and real-time data pipeline architectures.

Education

Bachelor's DegreeBachelor's degree or higher in Computer Science, Information Systems, Finance, Mathematics, Engineering, or a related quantitative field. Advanced degree preferred but not required with sufficient professional experience.

Experience

Data Engineering Leadership2+ years of direct management experience leading data engineering teams including hiring, performance management, coaching, and career development. Demonstrated success building high-performing technical teams and establishing engineering best practices.

Data Infrastructure at ScaleExperience building, scaling, and maintaining data infrastructure serving hundreds of users or petabyte-scale datasets. Track record of improving data quality, reducing pipeline latency, and enabling data-driven decision-making across organizations.

Stakeholder CollaborationProven ability to set technical direction and drive alignment across distributed engineering teams, product management, and business stakeholders. Experience translating ambiguous business requirements into well-scoped technical initiatives and communicating complex technical concepts to non-technical audiences.

Skills

Required skills

SQLAdvanced SQL for complex query writing, optimization, and performance tuning across relational database management systems

ETL and Data IntegrationHands-on expertise with ETL platforms and frameworks for building scalable data pipelines and data integration workflows

Apache AirflowExperience orchestrating complex data workflows and scheduling batch and streaming pipelines using Airflow or similar orchestration tools

BigQueryProficiency with Google Cloud's BigQuery for analytics, query optimization, and large-scale data warehouse operations

Data ModelingExpertise in dimensional modeling, schema design, and optimizing data structures for analytical workloads

Team LeadershipDemonstrated ability to hire, mentor, develop, and manage engineering teams while establishing technical standards and best practices

Stakeholder CommunicationStrong ability to translate between technical and business contexts, align teams around priorities, and communicate complex concepts clearly

Problem-SolvingStrong analytical and problem-solving skills with attention to detail and commitment to high-quality, production-grade solutions

Nice to have

Python or RProficiency in scripting languages for data analysis, automation, and rapid prototyping of data solutions

Fintech and Payment SystemsExperience in financial services, payment processing, cross-border payments, or fintech platforms demonstrating domain understanding

Kafka and Stream ProcessingHands-on experience with Apache Kafka, real-time event streaming, and stream processing architectures

Data GovernanceKnowledge of data governance frameworks, regulatory requirements in financial services, and data privacy/security practices

Databricks and SparkExperience with Databricks platform, Apache Spark, and distributed computing frameworks for large-scale data processing

Data CertificationProfessional certifications in data management, cloud platforms, or related technologies demonstrating continued professional development

Rapid Team ScalingTrack record of scaling data engineering teams during periods of significant organizational growth while maintaining quality and culture

Compensation & benefits

Salary

USD 200,000 – 335,000 (annual)

Stock options

Available

Benefits

Global Growth Opportunity

Join a hypergrowth fintech company valued at $11 billion with presence across 27 offices worldwide, offering unlimited professional development and exposure to international business challenges

Technical Leadership Impact

Drive architecture decisions and technical strategy at a company serving 250,000+ businesses, directly influencing product direction and platform capabilities

AI-First Culture

Work in an environment that prioritizes AI automation and modern data infrastructure, leveraging cutting-edge technologies to enhance team productivity and customer value

Collaborative Engineering Environment

Work alongside engineering leaders, product teams, and business stakeholders in a mission-driven organization focused on building the future of global banking

Competitive Compensation and Equity

Attractive salary package commensurate with experience and equity opportunities to participate in company growth

San Francisco Bay Area Location

Hybrid work arrangement based in San Francisco providing access to top technical talent, innovation hubs, and vibrant fintech ecosystem

Interview process

1
Initial Recruiter Conversation — 30-minute phone call with talent acquisition to discuss your background, management philosophy, and interest in leading data engineering at Airwallex. Opportunity to learn about team structure and role expectations.
2
Technical Leadership Interview — 60-minute conversation with current Engineering Manager or Director to assess technical depth in data modeling, ETL architecture, and system design. Discussion of past technical decisions and scaling challenges navigated.
3
Data Architecture Deep Dive — 60-minute technical interview focused on specific data engineering challenges including schema design, pipeline optimization, and data governance implementation. May include whiteboarding or design discussion of complex scenarios.
4
Management and Team-Building Interview — 60-minute conversation with Head of Data or VP Engineering to assess management capabilities, team development approach, and alignment with company culture. Discussion of hiring strategy, performance management, and building high-performing teams.
5
Cross-Functional Stakeholder Interview — 45-minute conversation with Product Manager or Business Stakeholder to assess communication ability, understanding of business context, and cross-functional collaboration skills in fast-paced environments.
6
Final Executive Round — 30-minute meeting with senior leadership (VP or C-level) to discuss long-term vision, company strategy, and cultural fit. Opportunity to ask final questions about company direction and team roadmap.