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