Senior Staff Data Engineer, Circle

• 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

Desirable

• You will define and drive the strategy for data reliability, quality, and operational excellence across the organization, shaping how Circle builds and trusts its data ecosystem
• This includes establishing company-wide standards for data quality, contracts, and governance; designing scalable reliability and observability frameworks; and institutionalizing incident management practices that promote a culture of accountability and continuous improvement
• You will influence platform and architectural decisions to ensure long-term scalability, reduce systemic risk, and eliminate classes of failure across the data landscape
• As a senior technical leader, you will also guide cross-team prioritization of reliability investments, define best-in-class data engineering practices, and 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