Senior Software Engineer, Circle

• Senior Software Engineer (III):
• 4+ years in software engineering for data systems
• Experience in scalable infrastructure to support batch, micro-batch or streaming processing
• Experience in business domains such as payment systems, credit cards, bank transfers, or blockchains
• Experience in data governance and provenance
• Internal knowledge of open-source data technologies
• Ability to tackle complex and ambiguous problems
• Self-starter who takes ownership and enjoys moving at a fast pace
• Excellent communication skills, with the ability to collaborate across multiple remote teams, share ideas and present concepts effectively
• Staff Software Engineer (IV):
• Includes all Senior Data Engineer requirements, plus:
• 7+ years in software engineering experience for large-scale and complex data systems
• Led 5+ person teams in architecture and system design
• Expertise in ETL/ELT, warehousing, feature engineering, modeling, or ingestion
• Ability to identify high-impact projects independently
• Strong experience in:
• Data warehouse architecture and design
• Integration of the data stack with other tools/services
• Implementing data quality checks

Desirable

• As a member of the Data Platform Engineering team, you own the core Data/ML platform, data ingestion and processing, ETL/ELT pipelines, data governance and security compliance, data analytics and visualization tooling, data modeling and warehousing
• These components power our Product, Engineering, Analytics, and Data Science teams by enabling experimentation, operational excellence, and actionable insights to accelerate business growth
• Design, build, and operate data platform services (warehousing, orchestration, and catalogs). Continuously enhance platform operations by improving monitoring, performance, reliability, and resource optimization
• Design, build and maintain the data ingestion framework to source the required data for various analytical and reporting needs, which include onchain data, internal system data, and partner data
• Be a domain expert in data warehousing, modeling, pipelines, and quality. Work closely across multiple stakeholders–including Product, Engineering, Data Science, Security and Compliance teams–on data contract modeling, data lifecycle management, governance and regulatory/legal compliance
• Provide ML data platform capabilities for AI/Data Science teams to perform data preparation, model training and management, and experiment execution
• Develop and maintain core services and libraries to enhance critical platform functionalities, such as cataloging data assets and lineage, tracking data versioning and quality, managing auto-backfilling, implementing access controls on data assets