Senior Software Engineer, Machine Learning

At Hive, we’re all about creating moments that matter and helping event marketers connect with their biggest fans. Our platform powers marketing for 1,500+ iconic events, festivals, venues, and promoters across North America. We help them grow their customer base and sell out shows using intelligent, automated, and personalized digital marketing tools.

Hive integrates with 25+ platforms (like Ticketmaster and Shopify) to provide rich customer data in real-time, enabling event marketers to engage their audiences with precision and impact.

What Data team at Hive looks like

Hive’s R&D Data team is responsible for how we store and query production data  aren’t focused on only BI or dashboards — we build the systems that power Hive’s products and make data accessible, reliable, and performant.

As a 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.

What you’ll get up to

• 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.

Our Tech Stack

• Programming:
  Python and Django

• Data Stores:
  Clickhouse, MySQL, MongoDB, Elastic Search, Redshift

• 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 and ML systems in production — high‑throughput event streams, real SLAs, and real consequences when things fail.

• Core ML foundations (supervised/unsupervised, cross‑validation, bias‑variance, regularization, eval metrics) and common algorithms (regression, tree ensembles, clustering).

• Feature engineering with Python ML tooling (pandas, scikit‑learn; familiarity with PyTorch or Tensor Flow).

• Production ML pipelines and feature datasets feeding model training and inference.

• MLOps practices: experiment tracking, model versioning/registry, deployment, and monitoring for drift/data quality.

• Strong foundations in distributed systems principles — partitioning strategies, consistency models, back pressure handling, fault tolerance, and capacity planning at 10x the volume you designed for.

• 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…