Work Mode: Remote
Time Zone: PST Working Hours
We are seeking a highly skilled Senior Data Engineer to design, build, and maintain scalable data platforms that support complex business and compliance use cases. This role partners closely with engineering, product, analytics, and compliance teams to enable reliable, high-quality data solutions—particularly within regulated environments such as AML, KYC, and Wealth Management.
Design, develop, and manage scalable ETL/ELT pipelines to support new product launches and evolving business needs.
Create new and optimize existing data models and schemas, including event-level, customer-level, and process-level datasets.
Build and maintain data quality, monitoring, and lineage frameworks to ensure reliability and transparency across the data ecosystem.
Monitor daily pipeline execution, troubleshoot issues, and resolve business-critical data incidents to meet SLAs.
Participate in an on-call rotation to support production data systems.
Partner with product managers, analysts, and other non-technical stakeholders to translate business requirements into robust data solutions.
Standardize business and product metrics across curated datasets.
Develop and maintain data dictionaries, documentation, and best practices.
Build self-service tools and frameworks to empower analytics and business teams.
Promote data engineering best practices and mentor team members as needed.
Snowflake
Databricks
dbt
GitHub
Airflow and/or Prefect
Tableau
Bachelor’s degree (or equivalent) in Engineering, Technology, or a related field.
Strong experience in regulated domains such as AML, KYC, and Wealth Management.
8+ years of experience with a Bachelor’s degree, or 6+ years with a Master’s degree, or equivalent practical experience.
Advanced proficiency in SQL.
Strong working experience with Python and Terraform.
Proven experience designing and delivering medium-to-large scale data engineering solutions, owning the full project lifecycle.
Hands-on experience with ETL orchestration tools (Airflow, Prefect) and dimensional data modeling.
Experience implementing data quality checks, observability, and lineage tracking.
Familiarity with financial crimes compliance systems and processes (strong plus).
Experience supporting AI/ML initiatives or data platforms used for intelligent automation (plus).
Excellent collaboration and communication skills, with the ability to explain technical concepts to non-technical stakeholders.
Ability to thrive in a fast-paced environment with evolving priorities.