Java Databricks Developer

Bounteous; Missouri; On-site; USD 90,000 - 130,000; Full time

Job summary

Bounteous seeks an experienced Java & Databricks Developer to design, build, and maintain scalable data applications and pipelines in a modern cloud data platform. You will work on ETL/ELT processes, streaming and batch jobs, and collaborate with data engineers, software developers, and business teams to deliver high-performance solutions.

The role emphasizes Java, Spark, Databricks, SQL, Delta Lake, and data quality, with a focus on security, CI/CD, and production support.

Qualifications

• Experience with Java, Spark, and Databricks to design and build data pipelines.
• Strong SQL and cloud-based data platforms experience.
• Understanding of data engineering concepts and distributed processing.

Responsibilities

• Design, develop, and maintain applications and data pipelines using Java, Spark, and Databricks.
• Develop scalable ETL/ELT pipelines with Databricks and Spark.
• Write efficient Java and SQL for data processing and transformation.

Skills

Java

Apache Spark

Databricks

SQL

Cloud platforms

Data engineering

Distributed processing

Tools

Databricks notebooks

Delta Lake

Spark SQL

Workflows/Jobs

Job description

We are looking for an experienced Java & Databricks Developer to design, develop, and maintain scalable data applications and data pipelines. The ideal candidate will have strong hands-on experience in Java, Apache Spark, Databricks, SQL, and cloud-based data platforms, with a good understanding of data engineering and distributed processing.

Key Responsibilities

• Design, develop, and maintain applications and data pipelines using Java, Apache Spark, and Databricks.
• Develop scalable and high-performance ETL/ELT pipelines using Databricks and Spark.
• Write efficient Java and SQL code for data processing and transformation.
• Work with Databricks notebooks, Delta Lake, Spark SQL, and workflows/jobs.
• Integrate data from multiple sources such as databases, APIs, files, and cloud storage.
• Develop and optimize batch and streaming data pipelines.
• Implement data quality, validation, error handling, and monitoring.
• Optimize Spark jobs and Databricks workloads for performance and cost.
• Collaborate with data engineers, software developers, architects, and business teams.
• Participate in code reviews, testing, deployment, and production support.
• Follow best practices for security, version control, CI/CD, and documentation.