Data Engineering Manager

Airwallex•San Francisco, CA

26d•$200,000 - $335,000•Hybrid

About The Position

We’re looking for a Data Engineering Manager to lead a team within Knowledge Platform 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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.