Databricks Developer

Trinetix is looking for a skilled Databricks Developer with strong expertise in Apache Spark, Python, and modern data engineering practices. This role involves designing, developing, and maintaining scalable data ingestion, transformation, and analytics pipelines using Databricks and related technologies. The ideal candidate is analytical, detail-oriented, and experienced in working with large-scale data, performance optimization, and reliable data processing solutions.

Requirements

5+ years of hands-on software development experience

Strong expertise in Apache Spark, including hands-on experience delivering solutions on Databricks

Advanced Python and data engineering skills, including strong experience with PySpark, Pandas, and related libraries

Experience developing and maintaining unit tests for Databricks workloads

Solid understanding of columnar storage formats, such as Parquet

Hands-on experience with Delta Lake and Delta Tables

Proven experience working with small to large data volumes, including performance tuning and optimization

Experience building data ingestion, transformation, and analytics pipelines

Hands-on experience with Databricks Workflows

Strong analytical and problem-solving skills with close attention to detail

Conversational English — B2 level or higher

Conversational Ukrainian

Nice-to-haves

Familiarity with Databricks DevOps practices

Experience with CI/CD processes for data engineering workloads

Understanding of software development lifecycle and engineering best practices

Experience working with distributed data processing environments

Experience working in cross-functional, distributed teams

Familiarity with enterprise-level data platforms and large-scale data environments

Core Responsibilities

Design, develop, and maintain scalable data ingestion, transformation, and analytics pipelines using Databricks

Develop data processing solutions using Apache Spark and PySpark

Work with large-scale datasets and implement performance tuning and optimization strategies

Design and maintain Delta Lake / Delta Table solutions to support reliable and scalable data processing

Develop and maintain unit tests to ensure the quality and reliability of Databricks workloads

Implement and manage Databricks Workflows for data processing and pipeline orchestration

Work with columnar storage formats such as Parquet and optimize data processing and storage solutions

Analyze business and technical requirements and translate them into effective data engineering solutions

Collaborate with cross-functional teams to ensure data pipelines and solutions meet technical and business requirements

Apply data engineering and software development best practices to deliver maintainable, scalable, and reliable solutions