Lead Platform Engineer

Lead Platform Engineer, AI-Native — Full-Stack + Data/ML Pipelines + DevOps Company: Revenue Roll Inc. / Tie Location: Fully remote, Canada-based Engagement Type: B2B contractor Reports to: CTO / Co-Founder About Tie Tie builds high-scale data infrastructure for digital commerce: identity resolution, customer intelligence, marketing analytics, and the activation systems that turn anonymous traffic into revenue for 200+ brands. Our platform spans customer-facing SaaS, real-time data and ML pipelines, multi-cloud infrastructure, and automation-heavy internal tooling. We're a Series A company heading into a Series B, and our engineering org is deliberately small, deeply automated, and ships like a team three times its size. About the Role We're hiring one Lead Platform Engineer to operate as a hybrid force multiplier across product engineering, data/ML pipelines, infrastructure, and AI-assisted development. This single hire consolidates work currently spread across contractors and vendors into one high-agency, Canada-based engineering owner. You should be senior enough to own production end to end, fluent enough in data and streaming systems to own the pipelines that the business runs on, pragmatic enough to ship application code, and AI-native enough to dramatically increase throughput using tools like Claude Code, Cursor, and agentic workflows. This is a hands-on technical leadership role, not a management role. You Can Credibly Say Yes to All Five This is the bar. If any one of these is a "no," the role isn't a fit: I can ship React/Node/Mongo product features and build the data/ML pipelines (Kafka, ClickHouse) myself. I can own Terraform/Kubernetes/GCP/AWS/CI-CD without leaning on a vendor. I've debugged production systems under pressure. I use AI coding tools every day and can walk through my workflow. What You'll Own Data & ML Pipelines (core) Own the systems the business runs on: real-time event ingestion and streaming through Kafka, analytical and feature data in ClickHouse, and the transformation and ML workflows on top. You'll design, operate, debug, and scale these pipelines — partitioning, retention, schema evolution, backfills, and the reliability of data moving end to end. Platform Engineering Build, refactor, and scale core product surfaces — React frontends, Node/Express/Nest-style services, and MongoDB-backed APIs — plus the integrations, onboarding, billing, and customer-facing analytics that sit on top. Infrastructure & DevOps Take ownership of CI/CD (GitHub Actions), Terraform, Kubernetes, AWS/GCP infrastructure, secrets management, deployment reliability, and environment automation. You own the path from commit to production, and the cost and reliability of what runs there. AI-Native Engineering Use AI aggressively and responsibly to accelerate delivery — scaffolds, tests, migrations, code review, observability queries, runbooks, Terraform modules, and data-debugging workflows — while applying senior engineering judgment, security discipline, and production rigor. Security, Compliance & Reliability Own practical SOC 2 readiness, audit-friendly CI/CD, secrets rotation, IAM hygiene, observability (OpenTelemetry, Sumo Logic), and incident response. What You'll Do in the First 90 Days Map the current platform, repos, environments, pipelines, cloud resources, and deployment paths. Take over day-to-day ownership of the data/ML pipelines (Kafka, ClickHouse) and high-priority full-stack tickets. Harden CI/CD for critical services: tests, build validation, deployment controls, and rollback procedures. Establish an AI-assisted engineering workflow the rest of the team can use — repeatable prompts, review workflows, test generation, and infra/debugging agents. Improve production and pipeline observability across services, containers, and data flows. Replace vendor-dependent infrastructure with durable, internally-owned Terraform/Kubernetes automation. Required Background 7+ years of software engineering experience, including production ownership. Hands-on ownership of data/streaming pipelines — Kafka and ClickHouse (or directly comparable) in production, including the operational side (throughput, retention, schema, backfills, reliability). Strong full-stack experience with TypeScript/JavaScript, React, Node.js, Express and/or NestJS, and MongoDB. Real cloud infrastructure depth: AWS and/or GCP, Terraform, Kubernetes, Docker, CI/CD (GitHub Actions), secrets management, deployment automation — owned as your responsibility, not handed to a separate ops team. Proven experience operating production systems with logs, metrics, traces, alerts, and incident response under pressure. Demonstrably AI-native: real work where AI tooling multiplied what you shipped, and a workflow you can walk through. Strong architectural judgment — can design systems, simplify them, and explain tradeoffs. Comfort in a lean startup environment with high ownership, incomplete documentation, and shifting priorities. Strong Plus ML/feature pipelines, model serving, or data-quality tooling at scale Snowplow/SnowcatCloud, BigQuery, dbt, Neo4j, RisingWave, Airbyte OpenTelemetry, Sumo Logic, Grafana, Prometheus Auth0, 1Password CLI, SOC 2, GitGuardian, TruffleHog, security automation Experience modernizing contractor- or vendor-built systems into durable internal infrastructure Prior founding engineer, staff engineer, DevOps lead, or architect experience Martech, adtech, e-commerce, customer data platforms, identity graphs, billing, or fintech workflows What "Great" Looks Like A senior IC who can lead without a team beneath them. Full-stack enough to ship product, pipeline-literate enough to own the data the business depends on, infrastructure-literate enough to own production. AI-native but not reckless. Comfortable replacing vendor dependency with internal leverage. Fast, opinionated, pragmatic, and able to debug across frontend, backend, data pipeline, cloud, and deployment layers. Interview Process Because the role is multifaceted, the loop is tailored to each candidate rather than a fixed gauntlet: a CTO screen, a technical deep dive, a short AI-assisted practical (work through part of a repo with AI, propose a change, add tests, explain the risks), and a domain session matched to your strengths — a Kafka/ClickHouse pipeline design for data-heavy candidates, a React/Node build for product-heavy ones — plus an infra/incident scenario, and a final founder working session. Revenue Roll Inc. (DBA Tie) is an equal opportunity employer.