Principal Software Engineer - Platform & Data Systems- Hybrid

A venture-backed team is building a regulated, data-intensive software platform that turns complex, manual workflows into automated, insight-driven systems. The product sits at the intersection of workflow automation, auditability/compliance, and “AI where it actually helps”—not just glue code around APIs. This is a high-trust environment: direct feedback, low ego, high ownership. You’ll be the senior technical force who can set the bar, unblock others, and still ship meaningful code every week. What you’ll own Architecture & Technical Direction (Python-first) • Own the evolution of a Python-based, service-oriented architecture built for performance, scale, and long-term maintainability • Make pragmatic calls on frameworks, tooling, and trade-offs (speed vs. correctness vs. future-proofing) • Establish patterns for observability, reliability, and audit-ready systems across services Hands-on Delivery • Take ambiguous problems and turn them into clear technical plans and working software • Build backend services in Python for data ingestion, transformation, APIs, and workflow orchestration • Drive rapid feedback loops from prototype → production without cutting corners that matter in regulated environments Quality, Security & Operations • Implement (or harden) CI/CD, environment strategy, and deployment practices for Python services • Raise the baseline on security best practices, access controls, and operational readiness • Own incident response standards, uptime expectations, and “how we build” discipline Mentorship & Engineering Culture • Be the technical multiplier: mentor others, review deeply, and improve how decisions get made • Help shape a culture of ownership, accountability, and learning (without bureaucracy) What we’re looking for • Proven experience building and scaling Python backend systems for data-heavy SaaS • Strong systems fundamentals: you can design, debug, and optimize across services, data flows, and infrastructure • Comfortable with cloud infrastructure, CI/CD, and modern operational practices • Strong instincts around reliability, observability, traceability, and auditability • Practical view of AI/ML: you understand where it adds leverage and where it introduces risk/noise • You’ve operated in early-stage or high-growth environments and you’re comfortable with ambiguity and pace Nice-to-haves • Strong data engineering instincts (pipelines, orchestration, schema design, performance tuning) • Experience with search / ranking / recommendation or complex data modeling • Built intelligent automation features using ML/AI in real-world systems • End-to-end leadership: roadmap shaping, delivery ownership, quality bar, stakeholder alignment Why this role • Hard, meaningful engineering problems with real-world stakes • High ownership: you’ll help define the technical foundation and standards • Room to grow into long-term technical leadership as the team scales