Senior Manager, Data Platform - T0
Engineering Manager · Manager · Full Time
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Role
What you'll do.
Senior Manager leading Airwallex's data platform engineering team, responsible for designing the technical strategy and architecture for high-volume financial data ingestion, Databricks lakehouse infrastructure, and real-time data orchestration serving 676,000+ businesses globally. This role combines deep technical expertise in distributed systems, data modeling, and production pipelines with strong people leadership to build a high-performing team and drive platform excellence across correctness, auditability, performance, and operational reliability.
Responsibilities
- Build and mentor high-performing data platform engineering team: Lead, grow, and develop a technical team of senior engineers and technical leads. Provide strategic technical direction, mentorship for career progression, clear ownership models, and foster an autonomous, high-performing engineering culture. Establish team standards around code quality, testing, documentation, and operational excellence.
- Define data ingestion and collection strategy: Set technical strategy for high-volume financial data ingestion from payment processors, banks, billing systems, and ERP/GL platforms. Design systems with strong guarantees around correctness, replayability, recovery, and reliability. Own rate-limit handling, idempotency, failure recovery, and large-scale backfill architectures supporting mission-critical financial operations.
- Architect and evolve Databricks lakehouse platform: Lead the evolution of the lakehouse using Spark, Delta Lake, Unity Catalog, dbt, Python, and SQL. Optimize for scale, performance, reliability, and cost efficiency. Design data contracts, schema evolution strategies, drift detection, and data quality frameworks. Establish canonical financial models that transform complex source data into consistent financial objects.
- Drive real-time data processing evolution: Lead platform evolution from batch toward near-real-time processing using Kafka, Change Data Capture (CDC), micro-batching, and incremental processing patterns. Implement Dagster-based orchestration for freshness, partitioning, and incremental computation. Balance performance gains against operational complexity and cost implications.
- Own data platform production foundations: Partner with infrastructure and platform teams to build reliable production infrastructure. Own architecture and implementation of observability, alerting, incident response, recovery procedures, and operational readiness. Design for high availability, graceful degradation, and rapid incident response in mission-critical financial systems.
- Guide infrastructure and platform strategy: Set technical direction for infrastructure across GCP, Terraform, Kubernetes/GKE, Helm, and GitOps/CI-CD practices. Lead infrastructure decisions supporting scalable, reliable data pipelines. Partner with platform teams to align data platform needs with broader infrastructure strategy.
- Lead complex technical migrations: Own architecture and execution of large-scale migrations from legacy pipelines to the lakehouse. Design rigorous parity validation, staged rollout strategies, and clear rollback procedures. Balance migration velocity with risk management and maintain production stability throughout.
- Optimize performance and cost across data systems: Drive performance optimization and cost efficiency across PostgreSQL, Databricks, and large-scale analytical workloads. Analyze and optimize query patterns, storage efficiency, and compute utilization. Establish benchmarking practices and cost attribution models.
- Set technical direction and standards: Establish engineering standards through architecture reviews, design documents, and long-term platform roadmaps. Lead technical decision-making using first principles thinking. Champion AI-assisted engineering practices that improve team velocity while maintaining high standards for code quality, security, and reliability.
- Partner cross-functionally with product, accounting, and ML leadership: Translate business requirements from product, accounting, and ML teams into scalable data platform capabilities. Collaborate with stakeholders to balance innovation, reliability, and time-to-market. Communicate technical trade-offs and platform constraints effectively to non-technical audiences.
Qualifications
What we look for.
Technical
Distributed Systems and Data Platform Architecture
Deep expertise designing, building, and operating large-scale distributed data systems. Strong understanding of consistency models, fault tolerance, scalability patterns, and trade-offs between correctness and performance. Experience architecting end-to-end platforms serving billions of events or records.
Spark, Databricks, and Lakehouse Architecture
Hands-on expertise with Apache Spark, Databricks workspace management, Delta Lake format, Unity Catalog, data governance, and modern lakehouse architectures. Experience optimizing Spark jobs for performance and cost, managing compute clusters, and designing for incremental processing patterns.
Data Orchestration and Workflow Management
Strong experience with modern data orchestration platforms, particularly Dagster. Understanding of freshness SLAs, partitioning strategies, incremental computation, dependency management, and observable orchestration. Experience designing for data lineage and impact analysis.
Streaming and Event-Driven Architecture
Deep understanding of streaming platforms, particularly Apache Kafka. Experience with Change Data Capture (CDC) patterns, event schemas, micro-batching, stream processing frameworks, and building reliable event-driven systems. Understanding of ordering guarantees, exactly-once processing semantics, and backpressure handling.
Data Ingestion and Collection Pipelines
Proven expertise building production ingestion systems handling large-scale data collection. Experience designing for correctness with retry logic, idempotency, checkpointing, offset management, and failure recovery. Understanding of backfill strategies, schema evolution, and handling upstream service failures.
Python and SQL Proficiency
Strong programming skills in Python for data engineering tasks, ETL pipelines, and infrastructure automation. Expert-level SQL knowledge for analytical queries, complex joins, window functions, and performance optimization. Ability to reason about query execution plans and indexes.
Data Modeling and Schema Design
Expertise in designing normalized and dimensional data models for analytical workloads. Understanding of schema evolution strategies, backward compatibility, data contracts, and canonical data modeling approaches. Experience with tools like dbt for transformation orchestration and testing.
PostgreSQL and Database Optimization
Strong operational knowledge of PostgreSQL for OLTP workloads. Experience with query optimization, indexing strategies, replication, backup/recovery, and performance monitoring. Understanding of analytical workload optimization and tools for performance profiling.
GCP Platform and Infrastructure
Production experience operating systems on Google Cloud Platform. Familiarity with GCP services: Compute Engine, Cloud Storage, BigQuery, Cloud Run, Pub/Sub. Experience with Terraform for infrastructure-as-code, managing multi-region deployments, and GCP-specific operational practices.
Kubernetes/GKE and Container Orchestration
Hands-on experience with Kubernetes and Google Kubernetes Engine (GKE) in production environments. Understanding of pod scheduling, resource management, networking, storage provisioning, and Helm for package management. Experience with stateful applications and persistent volumes.
GitOps and CI/CD Pipeline Design
Experience designing and implementing GitOps workflows and continuous integration/deployment pipelines. Familiarity with tools like Helm, kustomize, and GitOps operators (ArgoCD, Flux). Understanding of deployment strategies, secrets management, and testing automation.
Observability, Monitoring, and Reliability Engineering
Strong understanding of observability principles: metrics, logs, traces, and their integration. Experience designing monitoring strategies, SLO/SLI frameworks, and alerting rules. Familiarity with observability platforms and building reliable systems with clear operational visibility.
Financial Data and Domain Knowledge
Understanding of financial data models, accounting principles, transaction processing, and regulatory compliance requirements. Familiarity with payment systems, ledger accounting, reconciliation, and audit trails. Appreciation for data correctness requirements in financial contexts.
Education
Bachelor's degree in Computer Science, Engineering, or related field
Formal training in computer science, software engineering, mathematics, or closely related discipline. Provides foundation in algorithms, data structures, system design, and theoretical computer science concepts. Equivalent professional experience and demonstrated expertise considered.
Experience
8+ years data platform or backend engineering
Extensive experience building and operating data platforms, backend systems, or infrastructure. Demonstrated progression from individual contributor through senior engineering levels with increasing technical scope and complexity.
3+ years leading engineering teams
Proven track record managing and developing senior engineers and technical leads. Experience building autonomous, high-performing teams with clear ownership models. Demonstrated impact on team growth, retention, and technical capability development.
Production data pipeline design and operation
Significant hands-on experience designing, building, and operating production data pipelines at scale. Experience managing failure scenarios, recovery procedures, and maintaining data quality SLAs. Understanding of operational complexity in production systems.
Large-scale data migration leadership
Experience leading or executing complex technical migrations involving data systems. Demonstrated ability to manage risk, validate correctness, stage rollouts strategically, and plan rollback procedures. Ability to balance velocity with stability.
Cross-functional technical leadership
Track record partnering with product, data science, accounting, and infrastructure teams to translate requirements into platform capabilities. Experience communicating technical constraints and trade-offs to non-technical stakeholders. Demonstrated influence across organizational boundaries.
High-volume financial data systems
Experience building systems handling high-volume financial transactions, payment processing, or accounting data. Understanding of correctness requirements, auditability, and regulatory compliance in financial contexts. Familiarity with payment processor integrations or banking protocols.
Skills
Required
Spark & Databricks
Production expertise with Apache Spark and Databricks platform; optimization of lakehouse workloads, Delta Lake, and cluster management
SQL & Python
Expert-level proficiency in SQL for analytical queries and optimization; strong Python programming for data engineering and infrastructure automation
Data Orchestration (Dagster)
Hands-on experience with Dagster for workflow orchestration, freshness SLAs, incremental computation, and data lineage management
Kafka & Streaming Architecture
Deep understanding of Kafka, event-driven architectures, CDC patterns, and building reliable streaming systems with exactly-once semantics
PostgreSQL Database Administration
Strong operational knowledge of PostgreSQL including query optimization, indexing strategies, replication, and performance tuning for both OLTP and analytical workloads
GCP Infrastructure & Terraform
Production experience with Google Cloud Platform services and infrastructure-as-code practices using Terraform for repeatable deployments
Kubernetes/GKE & Helm
Practical experience operating applications on Kubernetes and GKE with understanding of pod scheduling, networking, storage, and Helm package management
Engineering Leadership & Team Building
Proven ability to build, mentor, and scale high-performing engineering teams; establish technical standards; and drive organizational culture around ownership and excellence
System Design & Architecture
Strong ability to design scalable, reliable systems; conduct architecture reviews; manage technical trade-offs; and communicate complex concepts across teams
Observability & Production Operations
Strong understanding of monitoring, alerting, incident response, and reliability engineering practices for mission-critical systems
Preferred
dbt (data build tool)
Nice to haveFamiliarity with dbt for SQL-based transformation orchestration, testing, and documentation in analytics workflows
Unity Catalog & Data Governance
Nice to haveExperience with Unity Catalog, data lineage tracking, and governance frameworks in lakehouse environments
Change Data Capture (CDC)
Nice to haveExperience implementing CDC patterns using tools like Debezium, AWS DMS, or similar for real-time data replication
BigQuery & Cloud Analytics
Nice to haveFamiliarity with Google Cloud's BigQuery or similar cloud data warehouse solutions for analytics
GitOps & ArgoCD/Flux
Nice to haveExperience with GitOps workflows and tools like ArgoCD or Flux for declarative infrastructure and application deployments
AI-Assisted Engineering & LLMs
Nice to haveComfort adopting AI tools for code generation, testing, and documentation; understanding of AI capabilities and limitations in software development
Financial Systems & Domain Knowledge
Nice to haveBackground in fintech, payment systems, accounting systems, or regulatory compliance; understanding of transaction processing and audit trails
Distributed Tracing & OpenTelemetry
Nice to haveFamiliarity with distributed tracing, OpenTelemetry standards, and building observable systems across microservice architectures
Cost Optimization & FinOps
Nice to haveTrack record optimizing cloud costs, understanding of resource utilization, and participation in financial operations engineering practices
Compensation
Pay and benefits.
Base·USD 255,000 – 300,000
Full posting
Original listing.
About Airwallex
Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 676,000 businesses, including McLaren Racing, Qantas, SHEIN, and TikTok, use us, directly or through our platform partners, to run their financial operations or build and monetize financial products of their own.
We started in Melbourne in 2015 to build the infrastructure global commerce runs on. We're the regulated backbone behind global payments: not by accident, but by design. A decade plus, 85+ licenses, and a financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific.
We're co-headquartered in San Francisco and Singapore, with more than 2,300 people across 27 offices. We hire builders with founder-level energy, people who move fast with good judgment, dig in with real curiosity, and make calls from first principles rather than waiting to be told what to do. Read our operating principles to see it in full.
About the Team
This team designs intelligent, autonomous systems that eliminate financial operations for companies globally. We automate everything from bookkeeping and reconciliation to tax, payroll, forecasting, and document understanding: turning hours of work into seconds. The team blends deep research pedigree with proven industry experience. We operate with high standards, are tool-agnostic, and expect engineers to own problems end-to-end. We collaborate closely, push boundaries responsibly, and ship exceptional work quickly. You’ll solve unprecedented technical challenges using unique, end-to-end financial datasets no one else in the industry has.
About the Role
As a Senior Manager of Data Platform Engineering, you’ll lead the team responsible for the data backbone of our financial systems. You’ll set the technical direction for high-volume financial data ingestion, our Databricks lakehouse, data orchestration, canonical financial models, and the infrastructure that makes our data platform reliable, scalable, and increasingly real-time.
You’ll combine strong technical judgment with people leadership—building a high-performing engineering organization, establishing platform standards, driving major migrations, and partnering closely with product, accounting, and platform teams. You’ll help shape a data platform where correctness, auditability, performance, and operational excellence are foundational.
This role is based in San Francisco.
What You'll Do
Lead and grow a high-performing data platform engineering team, providing technical direction, mentorship, career development, and clear ownership.
Set the technical strategy for high-volume financial data ingestion across payment processors, banks, billing systems, and ERP/GL platforms, with strong guarantees around correctness, replayability, and recovery.
Lead the evolution of our Databricks lakehouse using Spark, Delta Lake, Unity Catalog, dbt, Python, and SQL, optimizing for scale, performance, reliability, and cost.
Define standards for data contracts, schema evolution, drift detection, data quality, and canonical financial models.
Evolve the platform from batch toward near-real-time processing using Kafka, CDC, micro-batching, incremental processing, and Dagster orchestration.
Own the architecture of canonical financial models that transform complex source data into consistent financial objects and relationships powering our accounting engine.
Partner with infrastructure and platform teams to build reliable production foundations across observability, alerting, incident response, recovery, and operational readiness.
Guide infrastructure strategy across GCP, Terraform, Kubernetes/GKE, Helm, GitOps, and CI/CD.
Lead migrations from legacy pipelines to the lakehouse, with rigorous parity validation, staged rollouts, and clear rollback strategies.
Drive performance and cost optimization across PostgreSQL, Databricks, and large-scale data workloads.
Partner with product, accounting, ML, and platform leadership to translate business needs into scalable data platform capabilities.
Set technical direction through architecture reviews, design documents, engineering standards, and long-term platform roadmaps.
Champion AI-assisted engineering practices that improve team velocity while maintaining high standards for code quality, testing, security, and reliability.
What You’ll Need to Have
8+ years of experience in data platform, backend, or infrastructure engineering, including significant experience leading engineering teams.
Proven experience managing and developing senior engineers and technical leads and building strong, autonomous teams.
Deep expertise in data platforms, distributed systems, data modeling, and production data pipelines.
Strong experience with Python and SQL, plus hands-on experience with Spark, Databricks, Delta Lake, dbt, and modern lakehouse architectures.
Experience building reliable ingestion systems with large-scale backfills, incremental processing, checkpointing, rate-limit handling, idempotency, and failure recovery.
Strong understanding of streaming and event-driven architectures, including Kafka, CDC, and micro-batching.
Experience with data orchestration platforms such as Dagster and designing for freshness, partitioning, and incremental computation.
Experience optimizing PostgreSQL and analytical workloads for performance and cost.
Experience operating production infrastructure on GCP, with familiarity with Terraform, Kubernetes/GKE, Helm, GitOps, and CI/CD.
Strong understanding of observability, reliability engineering, and production operations.
Excellent technical communication skills, with the ability to influence architecture and engineering decisions across teams.
Experience leading complex technical migrations and balancing long-term platform strategy with near-term business priorities.
Passion for building teams and infrastructure that power reliable, scalable, mission-critical financial systems.
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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