About the role

Senior Data Engineer building Hive’s cloud-native data and ML platforms for event marketers. Owning scalable pipelines, feature infrastructure, and LLM-powered audience-data systems.

Responsibilities

Design and own a cloud-native big data platform handling audience data for millions of attendees and billions of interactions annually
Design and own ML infrastructure, including feature stores, training pipelines, model serving, and monitoring
Build reliable, low-latency feature and model infrastructure
Own the full data pipeline from change data capture through validation, transformation, and denormalization
Connect data-system performance to customer impact and business outcomes
Treat data as a product by defining SLAs, improving data health, and enabling discoverability
Use AI coding agents such as Claude Code
Build LLM-powered pipelines and autonomous agents to enrich, classify, and act on audience data at scale
Troubleshoot complex ML systems and build durable solutions
Collaborate with product and engineering teams in an ambiguous, fast-changing environment
Help shape Hive’s data/ML infrastructure and team

8+ years of hands-on data engineering experience
Proven experience designing, building, and operating large-scale distributed data and ML systems in production
Experience with high-throughput event streams, production SLAs, and failure consequences
Knowledge of supervised and unsupervised learning, cross-validation, bias–variance, regularization, and evaluation metrics
Knowledge of regression, tree ensembles, and clustering algorithms
Python ML tooling experience, including pandas and scikit-learn
Familiarity with PyTorch or TensorFlow
Experience building production ML pipelines and feature datasets for model training and inference
MLOps experience with experiment tracking, model versioning/registry, deployment, and drift/data-quality monitoring
Strong distributed-systems foundations, including partitioning, consistency models, backpressure, fault tolerance, and capacity planning
Production experience applying LLMs and agentic systems in data or ML contexts
Product and commercial orientation with ability to connect technical decisions to customer impact and business outcomes
Stakeholder communication skills for non-technical audiences
Ability to operate independently in ambiguous, fast-changing environments
Strong troubleshooting skills for complex ML systems
Nice to have: end-to-end data-platform ownership or re-architecture experience
Nice to have: SaaS or event-driven product experience
Eligible to work in Canada without current or future employer sponsorship