Staff Architect

Key Responsibilities

Domain Architecture Ownership

• Own the architecture for a core domain of the platform, ensuring it meets scalability, reliability, security, and performance requirements.
• Drive end-to-end design for cross-service initiatives: clarify requirements, propose options, lead tradeoffs, and align stakeholders.
• Produce high-quality design artifacts (HLDs, LLDs, data flow diagrams, API contracts) and lead design reviews.

Delivery Enablement (Hands-on Leadership)

• Provide hands-on technical leadership through reference implementations, prototypes/POCs, and critical-path contributions when needed.
• Partner with engineering teams to translate architecture into executable plans (milestones, risks, dependencies, rollout strategy).
• Help teams build “operable” systems: instrumentation, alerting, runbooks, dashboards, and failure-mode thinking.

Data & Signal Processing (if domain-aligned)

• Lead architecture for ingestion/enrichment pipelines handling large-scale behavioral events and third-party data.
• Drive improvements to data quality, lineage, observability, and freshness SLAs.

AI/ML Serving (if domain-aligned)

• Guide architecture for feature pipelines and inference serving (batch and online), model monitoring, experimentation, and safe rollouts.
• Partner with data science and ML engineering to productionize reliable, scalable ML systems.

Multi-Tenant & Platform Foundations (if domain-aligned)

• Drive patterns for tenant isolation, tenant-aware configuration, partitioning strategy, and scalable onboarding.
• Identify and mitigate risks around compliance, data integrity, and access control.

Integration & Activation

• Lead architecture for integrations with CRM/MAP/GTM tools (Salesforce, HubSpot, Marketo, Outreach, etc.).
• Establish patterns for reliable data activation (idempotency, retries, backfills, versioning, and extensibility).

Engineering Excellence & Mentorship

• Raise the bar through design reviews, technical mentorship, and knowledge-sharing.
• Contribute to architecture standards and reusable patterns; participate in architecture forums/councils.
• Identify architectural risks early and drive mitigation plans.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 8+ years in software engineering with significant focus on distributed systems and system design.
• Demonstrated experience designing and delivering data-intensive, distributed, cloud-based systems.
• Strong understanding of:
  • Distributed systems, reliability, scalability, fault tolerance
  • Microservices/event-driven architectures
  • API design, data modeling, integration patterns
  • Observability and operability (metrics/logging/tracing, incident learnings)
• Experience with a major cloud platform (AWS, GCP, or Azure).
• Ability to influence across teams through clear communication, strong design rationale, and pragmatic decision-making.

Preferred Qualifications

• Experience in B2B SaaS, MarTech/AdTech/Revenue Tech, or similar data-heavy product environments.
• Experience with streaming/batch data systems (Kafka/Flink/Spark or equivalents).
• Familiarity with OLAP/analytics stores (ClickHouse/Redshift/BigQuery/Druid/Trino, etc.).
• Familiarity with ML platform concepts (feature pipelines, inference patterns, monitoring), even if not a full-time ML practitioner.
• Experience designing multi-tenant data and service architectures.

Compensation

Base Salary Range: $209,496 - $307,260 (USD). Additional compensation may include bonus, commission, stock options. Benefits include health insurance, 401K matching, PTO, self-care days.