Data Engineering Manager

AirwallexYesterday

Location

SG - Singapore

Type

Full Time

Salary

SGD 150,000 – 220,000

Level

Manager

Role

Engineering Manager

Posted

Jul 8, 2026

Full TimeManager

The role

Summary

Data Engineering Manager leading the Knowledge Platform team at Airwallex, a USD 8 billion fintech company. You'll build and scale foundational data infrastructure (Databricks, Spark, Kafka) and AI-ready systems serving 200,000+ global businesses. Requires 8+ years of data engineering experience with people management, strong technical depth in modern data stacks, and the ability to translate complex business requirements into reliable, scalable data solutions across regulatory reporting, analytics, and real-time decision-making systems.

What you'll do

Lead and mentor data engineering team: Lead, coach, and grow a team of Data Engineers while setting high standards for technical quality, execution, ownership, and career development. Foster a culture of ownership, continuous learning, and technical excellence within your direct reports.

Define technical strategy and architecture: Establish and communicate the technical direction for your team across critical domains including data modeling, pipeline architecture, data quality frameworks, observability, and dataset lifecycle management. Ensure alignment with broader Knowledge Platform and company data strategy.

Translate business requirements into scalable solutions: Partner with engineering leaders, product teams, analytics, risk, and operations stakeholders to prioritize high-impact data problems and translate ambiguous business needs into reliable, well-structured data solutions that support multiple downstream users and systems.

Design and deliver data pipelines: Drive the design and delivery of reliable batch and near real-time data pipelines using Spark, Airflow, and Databricks that support critical internal and product-facing use cases. Ensure pipelines meet SLAs and support real-time decision-making requirements.

Elevate engineering standards: Improve organization-wide engineering standards across SQL, dbt, Spark, Airflow, and Databricks through best practices in performance optimization, maintainability, testing strategies, documentation, and governance. Drive adoption of these standards across your team.

Enable cross-functional collaboration: Contribute to organization-wide planning and cross-team execution, especially where Data Engineering interfaces with Data Platform teams, AI enablement initiatives, and broader Knowledge Platform priorities. Facilitate seamless data product delivery.

What we look for

Technical

Modern data engineering stackStrong hands-on proficiency with modern data engineering technologies including Databricks, Apache Spark, Kafka for streaming, SQL for data modeling, and Airflow for orchestration. Deep understanding of lakehouse architectures and their operational patterns.

Data pipeline design and scalabilityDemonstrated expertise designing and operating scalable, production-grade data pipelines and datasets used by multiple downstream stakeholders and systems. Experience with both batch and near real-time data processing patterns at scale.

Data quality and governancePractical understanding of data quality frameworks, governance models, observability, reliability patterns, and operational excellence in production data environments. Experience with data lineage, access control, and compliance requirements.

End-to-end technical leadershipDemonstrated ability to lead complex technical projects from problem definition and architecture design through execution, rollout, and ongoing operational improvement. Strong problem-solving and technical decision-making capabilities.

Education

Bachelor's degree or higherBachelor's degree or higher in Computer Science, Information Systems, Mathematics, or a related technical field. Advanced degrees or equivalent professional experience strengthens candidacy.

Experience

8+ years data engineering experienceMinimum 8 years of hands-on data engineering experience building and operating data systems. Background should include designing data models, building ETL/ELT pipelines, and supporting analytics and operational use cases at scale.

People management experiencePrior experience in people management or team leadership roles. Demonstrated ability to mentor engineers, set technical direction, drive execution accountability, and build high-performing teams in fast-paced environments.

Cross-functional stakeholder managementStrong track record working effectively with technical and non-technical stakeholders across product, analytics, engineering, operations, and business functions. Proven ability to communicate complex technical concepts to diverse audiences.

Skills

Required skills

SQL and data modelingExpert-level SQL proficiency for querying, optimization, and dimensional data modeling. Ability to design efficient schemas for analytics and operational use cases supporting multiple downstream consumers.

Apache Spark and distributed computingDeep hands-on expertise with Spark for large-scale data processing. Strong understanding of distributed computing concepts, performance tuning, and optimization for batch processing workloads.

Databricks ecosystemStrong practical experience with Databricks-based data stacks, Unity Catalog for data governance, and lakehouse architectures. Familiarity with Delta Lake, Databricks SQL, and integration patterns with ML ecosystems.

Orchestration and data pipelinesProduction experience with Airflow or similar orchestration tools for scheduling, monitoring, and managing complex data workflows. Understanding of SLA management, retry logic, and pipeline observability.

Team leadership and coachingProven ability to lead technical teams, mentor engineers, conduct code reviews, establish technical standards, and drive team execution. Strong communication skills for both technical and non-technical audiences.

Data governance and observabilityHands-on experience with data governance frameworks, lineage tracking, access control, and data quality monitoring. Understanding of regulatory compliance requirements and operational excellence in data platforms.

Nice to have

Financial domain expertiseBackground supporting complex financial domains such as regulatory reporting, payment systems, risk analytics, or financial compliance data. Experience with domain-specific data challenges in fintech environments.

AI and ML data infrastructureExperience supporting AI, ML, or advanced analytics use cases through well-structured, trustworthy data foundations. Familiarity with feature stores, model serving infrastructure, and AI-ready data architectures.

High-growth technology environmentsLeadership experience in high-growth technology companies, scale-ups, or fast-paced fintech environments. Demonstrated ability to scale data systems and teams as organizational needs evolve.

Platform and infrastructure collaborationExperience working closely with platform or infrastructure teams on shared standards, tooling, developer workflows, and standardized best practices across engineering organizations.

Streaming and real-time systemsHands-on experience with streaming data platforms like Kafka, Confluent, or cloud-native streaming solutions. Understanding of real-time data patterns, event processing, and near-real-time analytics architectures.

Data compliance and regional requirementsExposure to regional data compliance requirements, cross-border data considerations, and global data residency constraints relevant to fintech and payments industry.

Compensation & benefits

Salary

SGD 150,000 – 220,000 (annual)

Stock options

Available

Benefits

Global fintech equity upside

As a manager at a USD 8 billion fintech company backed by top-tier investors including T. Rowe Price, Visa, Mastercard, and Sequoia, you'll have significant equity upside potential and the opportunity to participate in a rapidly growing global payments platform.

International exposure and career growth

Work across 26 global offices with 2,200+ team members. Access to accelerated learning, founder-like ownership, real impact on fintech infrastructure, and mentorship from experienced engineering leaders in a high-growth scale-up environment.

Technical leadership development

Lead a strategic team within the Knowledge Platform, directly shaping data engineering culture, standards, and technical direction. Opportunity to mentor emerging talent and build world-class data engineering practices.

Impact on 200,000+ businesses

Your data infrastructure decisions directly impact products and services used by leading companies including Brex, Rippling, Navan, Qantas, and SHEIN, as well as hundreds of thousands of other global businesses.

Cutting-edge technology stack

Work with modern data engineering technologies at scale including Databricks, Spark, Kafka, and emerging AI-ready infrastructure. Lead adoption of best practices and influence platform direction.

Mission-driven culture

Join a company building the global payments and financial platform of the future. Align your work with a clear mission and operating principles focused on impact, ownership, and solving complex problems.

Interview process

1
Recruiter screening call — Initial conversation with Airwallex recruiter to discuss your background, career motivations, data engineering experience, and people management history. Typically 30 minutes.
2
Technical manager interview — Deep dive with a senior data engineering or platform leader covering your hands-on technical expertise with Spark, Databricks, SQL, and orchestration tools. Discussion of past architecture decisions, scalability challenges, and approach to data quality and governance.
3
Leadership and collaboration discussion — Conversation with the hiring manager or team lead focused on your approach to team leadership, coaching, and cross-functional collaboration. Expect discussion of how you've driven technical standards, mentored engineers, and worked with stakeholders.
4
System design discussion — Technical evaluation involving designing a data pipeline or system architecture for a real-world problem at Airwallex. May include discussing tradeoffs in technology choices, scalability patterns, and operational considerations.
5
Cross-functional stakeholder conversation — Discussion with product, analytics, or platform engineering leaders to understand how you communicate technical concepts to non-technical audiences and partner across functions to deliver data solutions.
6
Final leadership conversation — Potential final round with senior leadership (Director or VP level) covering vision for the role, growth trajectory, cultural fit with Airwallex operating principles, and long-term career goals.