Senior Engineer, Data

This is a fully remote position, open to applicants in Canada.

📋 Description

• Develop our Data Platform: Create and manage a cloud-native big data platform that processes audience data for millions of attendees and billions of interactions annually. You are not merely constructing pipelines; you are establishing the foundation that influences the quality of every insight, recommendation, and decision made by Hive's customers.

• Build our ML Platform: Architect and oversee the infrastructure that transitions models from experimentation to production, including feature stores, training pipelines, model serving, and monitoring. You alternate between data engineering and ML engineering roles, ensuring dependable, low-latency access to the features and infrastructure necessary for confidently building and deploying models. When a model performance declines in production, you are the one who has implemented the observability to identify issues before customers notice.

• Take Ownership of the Complete Pipeline — and Its Business Implications: From Change Data Capture to validation, transformation, and denormalization — you manage the entire stack. Moreover, you comprehend the impact on customers when a pipeline is delayed, a metric shifts, or a model receives outdated data. You link the technical aspects with business implications.

• Treat Data as a Product: You do not simply deliver pipelines; you provide data products that internal teams and customers rely on, similar to a production API. You establish SLAs, prioritize data health, and design for discoverability.

• Build and Utilize Agentic Systems: You adopt an agentic engineering approach in all aspects of your work and creations. You leverage AI coding agents (e.g., Claude Code) to amplify your efforts. Additionally, you develop LLM-powered pipelines and autonomous agents that enhance, classify, and act upon audience data at scale.

⛳️ Requirements

• A minimum of 8 years of practical experience in data engineering, with a demonstrated history of designing, building, and managing large-scale distributed data and ML systems in production, including high-throughput event streams and real SLAs that have tangible consequences when failures occur.

• Strong understanding of core ML concepts (supervised/unsupervised learning, cross-validation, bias–variance trade-offs, regularization, evaluation metrics) and common algorithms (regression, tree ensembles, clustering).

• Proficiency in feature engineering using Python ML tools (pandas, scikit-learn; familiarity with PyTorch or TensorFlow is a plus).

• Experience with production ML pipelines and feature datasets that support model training and inference.

• Familiarity with MLOps practices: tracking experiments, model versioning/registration, deployment, and monitoring for drift and data quality.

• Strong grounding in distributed systems principles, including partitioning strategies, consistency models, backpressure management, fault tolerance, and capacity planning for 10x the anticipated volume.

• Experience implementing LLMs and agentic systems within production data or ML environments, whether for pipeline enrichment, classification automation, or developing autonomous workflow components.

• A product and commercial mindset — you consistently evaluate technical decisions through the lens of customer impact and business outcomes, coupled with the communication skills necessary to convey this to non-technical stakeholders.

🏝️ Benefits

• Competitive salary and equity: your compensation reflects your impact.

• Fully remote work: operate from the comfort of your own home.

• Flexible working hours: minimal meetings and no rigid 9-5 schedule.

• Comprehensive Health & Dental coverage, along with Parental Leave top-ups in addition to EI benefits.

• Unlimited vacation/PTO: promoting your happiness and well-being!