Senior Engineer, Data

hive.co

29d•Remote

About The Position

Hive’s R&D Data team is responsible for how we store and query production data at scale. We 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.

Requirements

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 TensorFlow).
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, backpressure 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 impact and business outcomes, and you have the stakeholder communication skills to make that case to non-technical audiences.
Comfortable operating independently and making progress in ambiguous, fast-changing environments
Biased toward action. You’re willing to make decisions with imperfect information and iterate quickly, communicating with other teams inside product and engineering
Skilled at troubleshooting complex ML systems and building durable solutions when things break
Excited to shape the future of Hive’s data/ML 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 in a fast-growing environment.
Background in SaaS or event-driven products where data systems directly power user-facing features.

Responsibilities

Design and own a cloud-native big data platform handling audience data for millions of attendees and billions of interactions a year.
Design and own the infrastructure that takes models from experiment to production — feature stores, training pipelines, model serving, and monitoring.
Own the full pipeline — from Change Data Capture through validation, transformation, and denormalization — and understand its business impact.
Ship data products that internal teams and customers depend on like a production API, defining SLAs, obsessing over data health, and building 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.