Data Engineer

PocketHealth•Toronto, ON

18d•Remote

About The Position

As part of the PocketHealth team, you will enable hospitals and clinics across North America and empower over 2 million patients to discover a better healthcare experience. PocketHealth is a patient-centric platform that enables hospitals and clinics to share imaging records digitally with patients, instantly and securely. The platform also facilitates sharing between hospitals and physicians, instant image retrieval, and automated importing capability, serving over 800 hospitals and clinics across North America. The company believes in placing patients at the center of the record release process for ethical, easy, and secure data movement. As a Data Engineer, you will build and maintain the data infrastructure that powers data-driven solutions, enhancing the healthcare experience for millions. You will work closely with a Senior Data Engineer, receiving direct mentorship and opportunities to develop skills across the full data engineering lifecycle. This role offers a clear path to grow in the intersection of data engineering and machine learning, requiring user empathy and a focus on product quality and speed.

Requirements

1–2 years of engineering experience in data engineering, analytics engineering, software engineering, or a related field, including strong internship or co-op experience.
Bachelor’s degree in Software Engineering, Computer Science, or a related field, or equivalent practical experience.
Strong fundamentals in Python and SQL.
Hands-on experience working with structured data in an internship, research, academic, or early-career industry setting.
Strong problem-solving skills and attention to detail, with a focus on delivering high-quality solutions.
A collaborative mindset, strong communication skills, and eagerness to learn in a fast-paced environment.

Nice To Haves

Exposure to Databricks or similar modern data platforms.
Practical exposure to machine learning concepts such as model training, feature engineering, or evaluation.

Responsibilities

Build and maintain data models and transformation workflows that support analytics, reporting, and product use cases.
Support the development and improvement of data pipelines and systems to process and analyze healthcare data, gaining experience across the full data engineering lifecycle from ingestion and transformation to storage and consumption.
Work with Python and SQL to transform, validate, and troubleshoot data across our platform.
Contribute to our Databricks-based analytics platform and help improve the usability, quality, and documentation of our datasets.
Collaborate with cross-functional teams to understand their data needs and contribute to our data infrastructure roadmap.
Gain hands-on exposure to machine learning workflows - from data preparation and feature engineering to model training - with room to grow deeper as the team and your skills evolve.