Data Engineer

Role overview

A data engineering role on a team that builds the analytics backbone for an IoT platform serving industries such as agriculture, construction, transportation, and manufacturing. The position focuses on designing and maintaining large-scale data pipelines that turn device and software signals into reliable, analytics-ready datasets used for statistical analysis, machine learning, and dashboards. This is a remote opportunity open to candidates based in Canada, with no relocation support provided.

Responsibilities

- Build and operate highly reliable computed tables in a central data lake, blending structured product data, customer metadata, and unstructured inputs such as video and audio. - Develop and maintain data pipelines primarily using SparkSQL and PySpark, ensuring strong uptime and predictable performance. - Ingest external sources via APIs, and integrate them with internal datasets to support downstream analytics and modeling. - Partner with data scientists, analysts, ML/AI teams, and fellow engineers to deliver high-quality data for causal inference, model training, and reporting. - Hold the bar for data quality across customer-facing datasets, including monitoring, incident response, and continuous improvement.

Requirements

- Bachelor's or Master's degree in Computer Science, Statistics, or a related discipline. - 4+ years of professional experience in a data engineering role. - Demonstrated experience designing data models at scale. - Proficiency building ETL pipelines that handle large data volumes. - Hands-on experience with Spark-based data platforms and at least one orchestrator such as Airflow, Dagster, or Prefect. - Strong command of SQL, Python, and working with REST APIs.

Benefits and work setup

- Remote role within Canada. - Flexible work model with office availability for those who prefer in-person collaboration. - Benefits mentioned in the source include a professional development stipend, comprehensive health coverage, and parental leave plans.