Senior Full Stack Java Developer with Databricks

Senior Full Stack Java Developer with Databricks

We are seeking a Senior Full Stack Java Developer with Databricks to design and develop a modern publication management system from the ground up. This role will contribute across the full technology stack, including user interfaces, APIs, business logic, and data engineering components, to deliver a scalable, intuitive, and maintainable platform supporting publication tracking, curation workflows, reporting, analytics, and integrations.

Key Responsibilities

• Design and develop end-to-end features across frontend, backend, and data layers.
• Build modern, responsive user interfaces for publication management and curation workflows.
• Develop and maintain backend services, APIs, and business logic using Java and related technologies.
• Design, develop, and optimize data processing pipelines using Databricks, Apache Spark, and Delta Lake.
• Collaborate with architects to implement modern architectural patterns and design standards.
• Translate business and user requirements into scalable technical solutions.
• Implement data models and persistence for complex bibliographic and metadata-driven systems.
• Develop and maintain ETL/ELT workflows for ingesting, transforming, and managing large volumes of publication and research data.
• Integrate with internal and external systems, data sources, and third-party platforms.
• Write clean, testable, and well-documented code.
• Participate in code reviews and contribute to engineering best practices.
• Troubleshoot, optimize, and improve application performance, data processing efficiency, and usability.

Required Skills & Experience

• Proven experience as a Senior Full Stack Software Engineer on enterprise-scale applications.

• Strong proficiency in:

  • Frontend: React (preferred), Angular, or Vue.js
  • Backend: Java (Spring Boot), REST APIs, Microservices
  • Data Engineering: Databricks, Apache Spark (PySpark/Spark SQL), Delta Lake

• 3+ years of hands-on experience with Databricks in enterprise environments.

• Experience designing, developing, and optimizing data pipelines and ETL/ELT processes using Databricks.

• Strong experience working with Apache Spark, Delta Lake, Databricks Workflows, and Lakehouse Architecture.

• Experience building and consuming RESTful APIs.

• Solid understanding of modern software development practices, including:

  • Microservices-based architectures
  • API-first design
  • Agile development methodologies

• Experience with relational and/or NoSQL databases and data modeling.

• Experience integrating Databricks with cloud platforms such as Azure, AWS, or Google Cloud Platform.

• Familiarity with cloud-native or hybrid deployment environments.

• Strong understanding of data governance, data quality, performance tuning, and monitoring within Databricks environments.

• Strong problem-solving skills and attention to detail.

• Excellent communication and collaboration skills.

Preferred Qualifications

• Experience with greenfield development and phased delivery models.
• Familiarity with publication management systems and research information systems.
• Knowledge of scholarly metadata standards such as DOI, ORCID, MeSH, PubMed, or similar.
• Experience building dashboards, reports, analytics, or data visualization solutions.
• Experience with Unity Catalog, Delta Live Tables (DLT), MLflow, and Databricks Lakehouse Platform.
• Databricks Certification (Associate or Professional Data Engineer) is a plus.
• Background in research, healthcare, academic, or life sciences environments.

Nice-to-Have

• Exposure to AI/ML-assisted features, such as metadata enrichment, intelligent classification, or recommendation systems.
• Experience with CI/CD pipelines, containerization, and DevOps tooling.
• Understanding of security, accessibility, and data governance best practices.

What Success Looks Like

• Delivery of intuitive, high-quality user interfaces, robust backend services, and scalable Databricks-based data solutions.
• Well-architected, maintainable code and data pipelines supporting long-term scalability.
• Strong collaboration with architects, analysts, data engineers, and stakeholders.
• Contributions that measurably improve usability, efficiency, analytics capabilities, and system reliability.

Must-Have Technologies: Java, Spring Boot, React, Databricks, Apache Spark, SQL, REST APIs, Microservices, Cloud (Azure/AWS).