Senior Data Engineer (Seattle)

Airwallex•Seattle, WA

10d•$140,000 - $240,000•Hybrid

About The Position

Our Strategic Data team builds the data foundations that support business intelligence, machine learning, and operational decision-making. You'll help create reliable, scalable data models and pipelines while improving how teams use data across the business. Design and implement robust, scalable data models that support business intelligence, machine learning, and operational needs. Select appropriate schema designs—including star schema, snowflake, normalized, and denormalized models—based on the use case. Partner with business teams to translate data needs into clean, structured, and well-documented models, while promoting a single source of truth across data layers and pipelines. Build and maintain batch and streaming ETL pipelines across the full data workflow, from ingestion and transformation through delivery. Maintain data consistency, traceability, and quality across multiple data sources and domains, including distributed or multi-datacenter environments where relevant. Contribute to data governance strategies, policies, and standards across areas such as data quality, stewardship, metadata, master data, privacy, security, and lifecycle management. Work with Data Platform Engineers and Product Managers to identify root causes of data issues and deliver efficient, scalable solutions. Explore practical ways for data engineering and AI to work together across our products and operations. This role is based in Seattle, and you'll be expected to work from the office three days a week.

Requirements

Bachelor's degree or higher in Computer Science, Information Systems, Finance, Mathematics, or a related field.
Five or more years of experience designing and implementing ETL pipelines using tools such as Informatica, Talend, Apache NiFi, or similar data integration platforms.
Strong experience with data modeling, data schemas, ETL, and end-to-end data workflows.
Proficiency in SQL, database management systems such as MySQL, PostgreSQL, or Oracle, and data warehousing solutions.
Familiarity with Google Cloud Platform, particularly BigQuery and Airflow.
Excellent problem-solving skills, attention to detail, and commitment to producing high-quality work.
Strong communication and collaboration skills, including the ability to work effectively with globally distributed teams.
Strength in at least one core area of data engineering, particularly data modeling or ETL, with the ability to contribute across the broader data engineering discipline.

Nice To Haves

Experience in financial services, payments, or fintech.
Knowledge of data governance practices and regulatory requirements in the financial industry.
Experience with scripting languages such as Python or R for data analysis and automation.
Experience working with distributed or multi-datacenter data systems, including data migration, duplication, and consistency challenges.
Certification in data management or related technologies.

Responsibilities

Design and implement robust, scalable data models that support business intelligence, machine learning, and operational needs.
Select appropriate schema designs—including star schema, snowflake, normalized, and denormalized models—based on the use case.
Partner with business teams to translate data needs into clean, structured, and well-documented models, while promoting a single source of truth across data layers and pipelines.
Build and maintain batch and streaming ETL pipelines across the full data workflow, from ingestion and transformation through delivery.
Maintain data consistency, traceability, and quality across multiple data sources and domains, including distributed or multi-datacenter environments where relevant.
Contribute to data governance strategies, policies, and standards across areas such as data quality, stewardship, metadata, master data, privacy, security, and lifecycle management.
Work with Data Platform Engineers and Product Managers to identify root causes of data issues and deliver efficient, scalable solutions.
Explore practical ways for data engineering and AI to work together across our products and operations.