Principal Java Data Engineer

Requirements

• At PointClickCare, we empower our people to be the architects of a smarter healthcare future; one that is human-first and accelerated by AI to create meaningful and lasting change.
• Employees harness AI as a catalyst for creativity, productivity, and thoughtful decision-making.
• By integrating AI tools into our daily workflows, collaboration is enhanced, outcomes are improved, and every team member has the proficiency to maximize their impact.
• It all starts with our hiring practices where we uncover AI expertise that complements our mission, and we continue to invest in training and development to nurture innovation throughout the employee journey.
• This is a hands-on leadership role, requiring the ability to enhance and implement batch and real-time data solutions already in progress, mentor other team members, and deliver both business and technical objectives through ambiguity and uncertainty.
• What your day-to-day will look like: -Lead and guide the design and implementation of scalable distributed systems based on Java microservices -Engineer and optimize data pipelines using solutions like Apache Hudi, Apache Trino, Azure ADLS -Collaborate cross-functionally with product, analytics, and AI teams to ensure data is a strategic asset -Advance ongoing modernization efforts, deepening adoption of event-driven architectures and cloud-native technologies -Drive adoption of best practices in data
• experience in software or data engineering, including a minimum of 4 years focused on data pipelines (batch and streaming) -Proven
• experience driving technical direction and mentoring engineers while delivering complex, high-scale solutions as a hands-on contributor -Strong understanding of event-driven architectures and distributed systems, with hands-on
• experience implementing resilient, low-latency pipelines -Practical
• experience with cloud platforms (AWS, Azure, or GCP) and containerized deployments for data workloads -Fluency in data quality practices and CI/CD integration, including schema management, automated testing, and validation frameworks (e.g., dbt, Great Expectations) -Operational excellence in observability, with
• experience implementing metrics, logging, tracing, and alerting for data pipelines using modern tools -Solid foundation in data governance and performance optimization, ensuring reliability and scalability across batch and streaming environments -Proven
• experience with Lakehouse architectures and related technologies, including Apache Hudi, Azure ADLS Gen2, HDFS, and other big data technologies (Trino, Databricks, Spark) -Strong collaboration and communication skills, with the ability to influence stakeholders and evangelize modern data practices within your team and organization. #LI-AV1 #LI-remote