Senior Staff Data Engineer

Circle•San Francisco, CA

3d•Remote

About The Position

Circle (NYSE: CRCL) is one of the world’s leading internet financial platform companies, building the foundation of a more open, global economy through digital assets, payment applications, and programmable blockchain infrastructure. Circle's platform includes the world’s largest regulated stablecoin network anchored by USDC, Circle Payments Network for global money movement, and Arc, an enterprise-grade blockchain designed to become the Economic OS for the internet. Enterprises, financial institutions, and developers use Circle to power trusted, internet-scale financial innovation. Circle is committed to visibility and stability in everything we do. As we grow as an organization, we're expanding into some of the world's strongest jurisdictions. Speed and efficiency are motivators for our success and our employees live by our company values: High Integrity, Future Forward, Multistakeholder, Mindful, and Driven by Excellence. We have built a flexible work environment where new ideas are encouraged and everyone is a stakeholder.

Requirements

Extensive experience designing and operating scalable data platforms with a focus on reliability, quality, and observability.
Experience leveraging AI tools and methodologies to design and implement the solutions.
Deep expertise in data architecture, including data modeling, pipeline design, and distributed data systems.
Proven ability to define and implement data quality frameworks, including SLAs, data contracts, and governance standards.
Strong experience establishing SLI/SLO frameworks, monitoring, and alerting for large-scale data systems.
Demonstrated ability to lead complex, cross-team technical initiatives and drive alignment across stakeholders.
Experience defining and scaling engineering best practices, including testing, CI/CD, and development standards for data systems.

Nice To Haves

Experience building or evolving data platforms in high-growth or highly regulated environments (e.g., fintech, payments, crypto).
Familiarity with modern data tooling ecosystems, including orchestration, transformation, metadata, and observability platforms.
Experience with technologies such as Astronomer (Airflow), BigQuery, dbt, Dataplex, Kubernetes, and programming languages like Python or Go, or comparable tools in the modern data stack.
Track record of influencing platform strategy, including build vs buy decisions and long-term architectural evolution.

Responsibilities

Define and drive the strategy for data reliability, quality, and operational excellence across the organization, shaping how Circle builds and trusts its data ecosystem.
Establish company-wide standards for data quality, contracts, and governance.
Design scalable reliability and observability frameworks.
Institutionalize incident management practices that promote a culture of accountability and continuous improvement.
Influence platform and architectural decisions to ensure long-term scalability, reduce systemic risk, and eliminate classes of failure across the data landscape.
Guide cross-team prioritization of reliability investments.
Define best-in-class data engineering practices.
Lead complex, high-impact initiatives in ambiguous environments—driving alignment, mitigating risk, and delivering robust, scalable data solutions.
Define and implement organization-wide data quality standards, including data contracts, SLAs, and governance frameworks across domains.
Design and scale reliability and observability frameworks, including SLI/SLO models, lineage tracking, monitoring, and alerting patterns.
Establish and evolve incident management practices, including severity models, escalation paths, on-call structures, and blameless postmortems.
Develop and standardize data engineering SDLC practices, including testing strategies, CI/CD, versioning, and reusable frameworks.
Drive cross-functional prioritization of reliability initiatives, balancing technical debt, operational health, and product delivery across teams.
Lead ecosystem-wide platform improvements, identifying architectural gaps, reducing fragmentation, and influencing build vs buy decisions.
Own and deliver complex, high-impact data initiatives, aligning stakeholders, mitigating risks, and driving scalable solutions in ambiguous environments.