Senior Staff Data Engineer

**Circle** Senior Staff Data Engineer; $225K–$290K + equity; United States (Remote)

**About Company** Circle is a global financial technology company at the forefront of the new internet of money. Our infrastructure — including USDC, a blockchain-based dollar — powers payments, commerce, and financial applications worldwide. We help businesses, institutions, and developers leverage breakthrough blockchain technologies to drive global economic prosperity and digital inclusion.

**About the Role** This is not a traditional pipeline-building or warehouse-focused Data Engineer role. Circle is looking for a Senior Staff IC who will define and drive the long-term strategy for data reliability, quality, and operational excellence across the organization — shaping how Circle builds, governs, and trusts its data ecosystem. The central question is: can this person identify systemic weaknesses in a complex data ecosystem and create the frameworks, standards, architecture, and operating mechanisms that prevent entire classes of failures across multiple teams? If the answer is primarily "they can build very sophisticated pipelines," this is not the right hire.

**Responsibilities** - Establish company-wide standards for data quality, contracts, ownership, and governance; drive adoption across producer and consumer teams. - Design and lead scalable reliability and observability frameworks (SLIs/SLOs, freshness/availability targets, alerting strategy, incident response, error budgets). - 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. - Mentor senior engineers and become the technical point person for ambiguous, cross-cutting data problems.

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

**Bonus Skills** - Experience building or evolving data platforms in high-growth or highly regulated environments (fintech, payments, crypto). - Familiarity with modern data tooling: orchestration (Astronomer/Airflow), transformation (dbt), warehouses (BigQuery), metadata/lineage (Dataplex, DataHub, Amundsen), observability (Monte Carlo, Great Expectations). - Hands-on with Kubernetes, Terraform, streaming platforms (Kafka), Python or Go. - Track record of influencing platform strategy — build-vs-buy decisions and multi-year architectural evolution. - Ledger, reconciliation, settlement, or regulatory reporting experience.

**Logistical Info** - Location: United States, remote. - Compensation: $225,000 – $290,000 base + equity. - Number of openings: 1-2 - **Other:** Full benefits and equity; US work authorization required by default (consistent with Circle's other US Eng roles).

**Ideal Background** - Archetype 1 — Data Reliability Architect: data engineering + SRE mindset, built SLOs / observability / incident systems, established reliability practices across teams. Extremely attractive. - Archetype 2 — Data Quality Platform Leader: data contracts, metadata, lineage, quality frameworks, governance, developer workflows — driven adoption org-wide. - Archetype 3 — Fintech Data Infrastructure Leader: ledger, settlement, reconciliation, payments, regulatory data — especially strong when combined with platform + reliability ownership. - Archetype 4 — Large-Scale Data Platform Staff Engineer: distributed data ecosystem, multi-team standards, platform evolution — must have direct quality/reliability relevance, not only infra scale. - Archetype 5 — Scaling-Company Platform Builder: has both big-company engineering rigor AND experience building systems in a rapidly scaling company. - Target companies (logos are a starting point, not evidence): Google, Meta, Amazon, Netflix, Airbnb, Uber, LinkedIn, Stripe, Coinbase, Block, PayPal, Robinhood, Plaid, Adyen, DoorDash, SoFi. - Calibration examples from prior submissions — Stronger signals: Kevin (DoorDash DE + streaming/governance/observability); Sufian Dar (ledger, reconciliation, regulatory). Mixed: Abner Correa (healthcare + dbt but inconsistent tenure). Rejected: Abhidutt Yerramilli (strong general DE, no specific data-quality evidence); Christopher Channing (generic bullets); Prateek (Monte Carlo founding eng but weak concrete accomplishments). Multiple rejections driven by short-tenure patterns. - Hiring manager: Ricardo Correa. Strongest filters are data quality, reliability, systems thinking, and Staff-level influence. He heavily discounts brand logos when the résumé doesn't explain what the person actually built.

**Green Flags** - Designed data quality frameworks used across multiple teams; established data contracts or schema ownership standards. - Reduced P0/P1 incidents materially; moved an org from reactive operations to systematic reliability engineering. - Established freshness/availability SLOs on hundreds of production datasets with shared alerting and ownership workflows. - Built automated reconciliation for ledger or settlement data at billion-dollar transaction scale. - Consolidated overlapping ingestion frameworks into a shared platform adopted by dozens of teams. - Standards or frameworks they authored were adopted outside their immediate team. - Meaningful tenure (multi-year) at one employer — long enough to design, ship, operate, and evolve systems. - Naturally frames answers as "why does this failure class exist and how do we prevent it everywhere?" rather than "how do we fix this pipeline?". - Regulated-industry exposure (financial services, healthcare, banking, insurance) demonstrating auditability / lineage / governance discipline.

**Red Flags** - "Sophisticated pipeline builder" as the headline story — no framework/standard/adoption evidence. - Tool-first résumés — Snowflake + Airflow + dbt + Kafka + Kubernetes + DataHub listed with no organizational problem attached. - Vague résumé language ("led enterprise data transformation", "drove data modernization", "improved platform reliability") without specifics on what they personally owned, who adopted it, and what changed. AI-generated summaries get very little weight. - Repeated 1–2 year tenures. One or two short roles is fine; a career of short stints is not. - Currently an engineering manager without unusually strong recent hands-on IC work — this is a Senior Staff IC hire, not a lead-a-team hire. - Founder/CTO with only generic "built the platform 0→1" descriptions and no personal technical ownership evidence. - Data Architect profile that's mostly diagrams / enterprise governance / consulting / vendor selection with limited engineering credibility. - Pure analytics engineers, warehouse-only engineers, ETL specialists, pure infra engineers, tool specialists — lower priority. - "Data platform" candidates whose work is really infrastructure engineering that happens to support data systems, with no connection to data trust, quality, or governance. - Exposure vs ownership: prior submission (~9 years at a major fintech, data mesh + Monte Carlo + 100+ teams on paper) was screened out because the HM interview exposed the depth didn't match the breadth. Probe aggressively: what did they personally design, decide, and change?

**Interview Process** 1. CodeSignal assessment. 2. Recruiter screen. 3. Recruiter/HM call with Jordan Richardson (CodeSignal sent by Jordan; role live Aug 27 — later stages not yet observed). 4. Technical interview: data architecture + system design. 5. Cross-functional panel with data platform, product, and infra partners. 6. Leadership / VP interview.