Senior Data Engineer, you’ll play a vital role in evolving our data platform, which directly determines what our customers can do, how fast our product moves, and how confidently leadership can make bets. You'll own outcomes, not tickets. If a business metric is off and it touches data, that's yours to care about. 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. 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.

Our Tech Stack AWS Services: DMS, RDS, Kinesis, Glue, Redshift; Programming: Python and Django; Data Stores: Clickhouse, MongoDB, ElasticSearch, Snowflake; Orchestration: Airflow or Dagster

What you bring 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; 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; A product and commercial orientation — you consistently frame technical decisions in terms of customer impact and business outcomes.

Who you are: Comfortable operating independently and making progress in ambiguous, fast-changing environments; Biased toward action; Skilled at troubleshooting complex systems; Excited to shape the future of Hive’s data infrastructure and team in a high-growth, fast-paced company.

Nice to haves: History of owning or re-architecting a data platform end-to-end; Background in SaaS or event-driven products where data systems power user-facing features.

Compensation/Benefits Package: Meaningful salary and equity: compensation range CAD 123,600 to 187,900 per year; New team members typically start between 123,600-155,000 based on experience; Work fully remote from home.

This is a remote role with Canada-based location. Remote work allowed.