Principal Java Data Engineer

Role: Principal Java Data Engineer

Location: Canada or US (Remote)- EST Working hours

Long Term contract

Job summary:

Value Technology is seeking a highly skilled Principal Java Data Engineer with 10+ years of experience in software and data engineering to lead the design, development, and optimization of enterprise-scale data platforms and distributed systems. The ideal candidate will possess deep expertise in Java-based microservices, modern data engineering practices, and large-scale batch and streaming data pipelines. This role requires strong hands-on experience with Lakehouse architectures, Apache Hudi, Trino, Azure ADLS Gen2, and cloud-native technologies, along with a proven ability to drive technical strategy and mentor engineering teams.

Mandatory skill: -    Spec-Driven Development (SDD) is Must with AI

Ideal Experience

• 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
• Principal Software Data Engineer with at least 10 years of professional 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 Google Cloud Platform) 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 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.