Senior AI Data Software Engineer

EPAM is a global leader in AI transformation engineering and integrated consulting, serving Forbes Global 2000 companies and startups. They have over thirty years of expertise in custom software, product and platform engineering, empowering clients to become AI-Native enterprises.

• 4+ years experience in software development with Data Software Engineering technologies
• Advanced knowledge of Python
• Proficient with Spark and Databricks
• Experience with cloud platforms such as Azure, Amazon Web Services, Google Cloud Platform
• Experience with AI development tools like Claude Code, Github Copilot, Cursor
• Strong understanding of data file formats like JSON, Parquet, Avro
• Hands-on experience with SQL and NoSQL databases including Cassandra and MongoDB
• Proficiency in data warehousing solutions and ETL processes
• Bachelor’s degree in Computer Science, Mathematics or related technical field
• Detail-oriented with excellent analytical and problem-solving skills
• Knowledge of software development methodologies and best practices
• Exceptional oral and written communication skills in English (B2+)

Nice to have:

• Experience with message queues, specifically Kafka, and large data transfer applications/middleware
• Production experience in search technologies such as Solr, OpenSearch, or Lucene
• Knowledge of Scala or Java

• Implement data ingestion from diverse sources including RDBMS, REST HTTP API, flat files, Streams, Time series data, and SAP
• Research and utilize Data Software Engineering technologies for effective data ingestion
• Process and transform data using tools such as Spark, Databricks, and various Cloud Services
• Participate in next waves of AI Adoption by implementing agentic development environments
• Implement project-specific business logic using programming languages supported by the primary data platform
• Optimize data retrieval, develop dashboards, and perform data validation
• Collaborate with data scientists, analysts, and IT professionals
• Provide thought leadership on Data Software Engineering best practices and technology trends
• Maintain integrity and security of data sources and platforms
• Perform troubleshooting, debugging, and upgrading of data systems and applications

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