Senior Software Engineer – Data & ML Platform

Job not on LinkedIn

🔥 13 hours ago

🇨🇦 Canada – Remote

⏰ Full Time

🟠 Senior

🚰 Data Engineer

👻 Ghost score 18%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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GoMaterials

WebsiteLinkedInAll Job Openings

11 - 50 employees

🏗️ Construction

📦 Logistics

🌾 Agriculture

💰 Seed Round on 2022-08

Construction • Logistics • Agriculture

GoMaterials is a company that optimizes wholesale plant and tree sourcing specifically for landscapers. Their services streamline the process of estimation and coordination by providing a zero-cost solution that connects landscapers with high-quality plants and trees through wholesale nursery partners. Landscapers can send their plant and tree lists in any format, receive competitive quotes within 48 hours, approve orders online, and benefit from dedicated account management. GoMaterials enhances efficiency by minimizing coordination hassles, reducing returns through strict quality control, and offering competitive pricing to increase bid win-rates. The company is trusted by the lawn and landscape industry across Canada and the US.

📋 Description

• Own, operate, and improve backend services running in Azure, including serverless services, batch workloads, and ML inference endpoints • Manage deployments and reliability across environments, including CI/CD, monitoring, alerting, incident response, and operational runbooks • Operate and optimize cloud compute environments, including autoscaling, container images, identity, and resource management • Improve production-system reliability, scalability, and maintainability • Design and build ETL/ELT pipelines from relational and document databases into analysis-ready datasets • Help develop a lakehouse-style analytical layer and supporting infrastructure • Build and maintain infrastructure-as-code across environments using Terraform or Bicep • Manage cloud infrastructure, storage, application hosting, identity and access management, Key Vault, and cost optimization • Implement monitoring, logging, data-quality checks, and freshness alerting across data workflows • Build internal services, APIs, and developer tooling • Build infrastructure and tooling for ML and Operations Research experimentation, deployment, evaluation, and reproducibility • Turn research prototypes into reliable production services and workflows • Own model packaging, versioning, deployment, and CI processes for ML and OR codebases • Build automated evaluation and benchmarking pipelines for model performance, drift, and reliability • Partner with Data Scientists and OR specialists to operationalize experiments • Establish practices for code quality, automated testing, version control, and CI/CD • Conduct peer code reviews and promote scalable engineering practices • Collaborate with Data Science, Operations Research, Product, and Engineering teams • Contribute to architecture, scalability, reliability, and performance decisions • Translate technical and business needs into pragmatic engineering solutions

🎯 Requirements

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or a related field, or equivalent practical experience • Strong programming skills in Python and SQL • Strong understanding of APIs, backend service design, and distributed systems • Experience building and operating production data pipelines end to end, including retries, idempotency, backfills, orchestration, and freshness monitoring • Hands-on experience designing and operating production services in Azure or another major cloud platform, with an interest in going deep on Azure • Experience with serverless and batch compute, object storage, identity and access management, and monitoring • Experience with infrastructure-as-code tools such as Terraform, Bicep, or ARM • Strong knowledge of Git, CI/CD, automated testing, and modern software engineering practices • Comfort working with ML or OR codebases and model artifacts, including reading, running, packaging, and deploying them • Experience owning live production systems and improving an existing codebase over time • Nice-to-have experience with Delta Lake, Parquet, lakehouse architectures, DuckDB, Polars, dbt, Azure Machine Learning, MLflow, DVC, Durable Functions, Airflow, Dagster, Prefect, Kubernetes, Azure data governance and security, DevOps/SRE, optimization, logistics, transportation, or large-scale ML systems

🏖️ Benefits

• Learning and promotion opportunities • Health benefits • Time off • Equity after the first year • Fun, tight-knit team environment • Culture code/team celebrations