Hive

1 day ago

Senior Engineer, Data

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 19 hours agoStatus: Live

Job Summary

Own Hive’s cloud-native data and ML platforms that power event marketing for millions of attendees. Build the infrastructure behind production audience data, end-to-end pipelines (including CDC, validation, and denormalization), and model infrastructure from experiment to production with monitoring and drift/data quality observability. Treat data as a product with SLAs and data health, and apply an agentic/LLM mindset to enrich, classify, and automate audience workflows.

Location: Toronto

Workplace: Remote

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Mid level

Key Responsibilities

•Design and own a cloud-native big data platform for large-scale audience data powering customer insights, recommendations, and decisions.
•Build ML platform infrastructure to move models from experiment to production, including feature stores, training pipelines, model serving, and monitoring.
•Own end-to-end data pipelines from change data capture through validation, transformation, and denormalization while connecting failures to business impact.
•Treat data as a product by defining SLAs, ensuring data health, and improving discoverability for internal teams and customers.
•Build and leverage agentic systems and LLM-powered pipelines/agents to enrich, classify, and act on audience data at scale.

Pay and Benefits

Equity and Bonus:Equity

Perks:Remote WorkHealth InsuranceDentalParental LeavePaid Leave

Key Requirements

•8+ years of hands-on data engineering experience building and operating large-scale distributed data and ML systems in production with real SLAs.
•Strong core ML foundations (e.g., supervised/unsupervised learning, cross-validation, bias–variance, regularization, eval metrics) and algorithms like regression, tree ensembles, and clustering.
•Feature engineering experience using Python ML tooling such as pandas and scikit-learn (and familiarity with PyTorch or TensorFlow).
•Experience building production ML pipelines and MLOps practices including experiment tracking, model versioning/registry, deployment, and monitoring for drift/data quality.
•Distributed systems fundamentals (partitioning, consistency models, backpressure, fault tolerance, capacity planning) and experience applying LLMs/agentic systems in production data or ML contexts.

Experience:8+ years

Skills:TroubleshootingStakeholder communicationIndependent executionDecision making

Tech Stack:PythonDjangoClickhouseMySQLMongoDBElasticSearchRedshiftAirflowDagsterPyTorchTensorFlowPandasScikit-learnLLMsClaude Code

Company Brief

Hive

Provides a marketing automation platform (email, SMS, CRM, ads) tailored to event promoters and ticketing partners to drive ticket sales and audience engagement through data-driven campaigns and automation.

Industry: Events & Live Entertainment

Company Size: Medium (51 to 250 employees)

Revenue: USD 1M to 5M

Headquarters: Kitchener, Canada

Founded: 2014

Glassdoor: 3.5

Website LinkedIn Glassdoor

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Hive

1 day ago

Senior Engineer, Data

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 19 hours agoStatus: Live

Job Summary

Location: Toronto

Workplace: Remote

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Mid level

Key Responsibilities

•Design and own a cloud-native big data platform for large-scale audience data powering customer insights, recommendations, and decisions.
•Build ML platform infrastructure to move models from experiment to production, including feature stores, training pipelines, model serving, and monitoring.
•Own end-to-end data pipelines from change data capture through validation, transformation, and denormalization while connecting failures to business impact.
•Treat data as a product by defining SLAs, ensuring data health, and improving discoverability for internal teams and customers.
•Build and leverage agentic systems and LLM-powered pipelines/agents to enrich, classify, and act on audience data at scale.

Pay and Benefits

Equity and Bonus:Equity

Perks:Remote WorkHealth InsuranceDentalParental LeavePaid Leave

Key Requirements

•8+ years of hands-on data engineering experience building and operating large-scale distributed data and ML systems in production with real SLAs.
•Strong core ML foundations (e.g., supervised/unsupervised learning, cross-validation, bias–variance, regularization, eval metrics) and algorithms like regression, tree ensembles, and clustering.
•Feature engineering experience using Python ML tooling such as pandas and scikit-learn (and familiarity with PyTorch or TensorFlow).
•Experience building production ML pipelines and MLOps practices including experiment tracking, model versioning/registry, deployment, and monitoring for drift/data quality.
•Distributed systems fundamentals (partitioning, consistency models, backpressure, fault tolerance, capacity planning) and experience applying LLMs/agentic systems in production data or ML contexts.

Experience:8+ years

Skills:TroubleshootingStakeholder communicationIndependent executionDecision making

Tech Stack:PythonDjangoClickhouseMySQLMongoDBElasticSearchRedshiftAirflowDagsterPyTorchTensorFlowPandasScikit-learnLLMsClaude Code

Company Brief

Hive

Industry: Events & Live Entertainment

Company Size: Medium (51 to 250 employees)

Revenue: USD 1M to 5M

Headquarters: Kitchener, Canada

Founded: 2014

Glassdoor: 3.5

Website LinkedIn Glassdoor