Principal Software Engineer - Big Data & Java

About the role

PointClickCare, a health technology company serving over 30,000 provider organizations, is looking for a Principal Software Data Engineer to contribute to all phases of the software development lifecycle. The role designs, develops and maintains large-scale data platforms and data pipelines built on microservices, and encourages technical excellence across the team. It is a hands-on leadership position for an individual contributor who mentors others and delivers business and technical goals through ambiguity.

The role is remote, with occasional travel to the Mississauga and/or Salt Lake City offices for onboarding, team events and annual meetings.

Key responsibilities

• Lead the design and implementation of scalable distributed systems based on Java microservices.
• Build and optimize data pipelines with tools such as Apache Hudi, Apache Trino and Azure ADLS.
• Work with product, analytics and AI teams so data becomes a strategic asset.
• Modernize the stack, deepen event-driven architectures and cloud-native technologies, and promote data governance, observability and performance tuning.
• Embed data quality in pipelines: schema contracts, transformation tests, data assertions, backward-compatible schema evolution, and automated checks for freshness, completeness and accuracy.
• Set up observability with metrics, logging, distributed tracing, SLAs and SLOs, alerting and dashboards.
• Promote quality through peer reviews and constructive feedback.

Required profile

• Principal Software Data Engineer with at least 10 years of experience in software or data engineering, including a minimum of 4 years on batch and streaming data pipelines.
• Proven track record of technical direction and mentoring while delivering complex, high-scale solutions as a hands-on contributor.
• Strong understanding of event-driven architectures and distributed systems, with resilient, low-latency pipelines.
• Experience with cloud platforms (AWS, Azure or GCP) and containerized deployments.
• Fluent in data quality practices and CI/CD, including schema management, automated testing and validation frameworks such as dbt and Great Expectations.
• Solid foundation in data governance and performance optimization, and proven experience with lakehouse architectures (Apache Hudi, Azure ADLS Gen2, HDFS, Trino, Databricks, Spark).