Staff Engineer leading AWS integrations, microservices, and AI automation for EverCommerce’s SaaS service - commerce platform. Designing scalable solutions and guiding engineers across U.S. and Canadian teams.
About the role
- Senior Data Engineer evolving Hive's data platform and building ML infrastructure for event marketing solutions. Responsible for processing vast amounts of audience data across platforms.
Responsibilities
- Build our Data Platform: Design and own a cloud-native big data platform handling audience data for millions of attendees and billions of interactions a year. You're not just building pipelines — you're building the infrastructure that determines the quality of every insight, recommendation, and decision Hive's customers make.
- Build our ML Platform: Design and own the infrastructure that takes models from experiment to production — feature stores, training pipelines, model serving, and monitoring. You switch hats between data engineering and ML engineering, ensuring reliable, low-latency access to the features and infrastructure we need to build and ship models confidently. When a model degrades in production, you're the one who built the observability to catch it before the customer does.
- Own the Full Pipeline — and Its Business Impact: From Change Data Capture through validation, transformation, and denormalization — you drive the stack end to end. But you also understand what breaks for a customer when a pipeline is late, a metric drifts, or a model gets stale data. You connect the technical dots to the business dots.
- Treat Data as a Product: You don't ship pipelines — you ship data products that internal teams and customers depend on like a production API. You define SLAs, obsess over data health, build for discoverability.
- Build and Leverage Agentic Systems: You bring an agentic engineering mindset to everything — both how you work and what you build. You use AI coding agents (e.g. Claude Code) as a force multiplier. And you build LLM-powered pipelines and autonomous agents that enrich, classify, and act on audience data at scale.
Requirements
- 8+ years of hands-on data engineering experience, with a proven track record of designing, building, and operating large-scale distributed data systems in production — high-throughput event streams, real SLAs, and real consequences when things fail
- Strong foundations in distributed systems principles — partitioning strategies, consistency models, backpressure handling, fault tolerance, and capacity planning at 10x the volume you designed for
- End-to-end ML engineering experience: feature engineering and feature store design, training pipeline orchestration, model deployment and serving infrastructure, and production monitoring including drift detection and retraining triggers
- Experience applying LLMs and agentic systems in production data or ML contexts — whether enriching pipelines, automating classification, or building autonomous workflow components
- A product and commercial orientation — you consistently frame technical decisions in terms of customer impact and business outcomes, and you have the stakeholder communication skills to make that case to non-technical audiences.
Benefits
- Meaningful salary and equity: you're rewarded based on impact.
- Work fully remote from the comfort of your home.
- Flexible work hours: minimal meetings and no 9-5
- Health & Dental coverage with Parental Leave top-ups in addition to EI benefits
- Unlimited vacation/PTO: so you can be happy and healthy!
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