We are looking for a strong Data Engineer to join our Context Service Data Team.
The role focuses on designing and building scalable data models, data pipelines, and ETL processes that power enterprise-wide AI and agent experiences.
The ideal candidate has hands-on experience with modern data platforms, including Apache Spark and Databricks, and is passionate about transforming complex enterprise data into high-quality, reliable, connected and accessible information assets. You will work closely with core and product teams to build the data foundation that enables intelligent agent workflows and contextual experiences at scale.
This opportunity is with Naya, an EPAM company, a leading global provider of data platforms and development professional services.
Based in Israel, Naya is one of the fastest-growing companies in the data and development technology space, and we’re growing our team.
Responsibilities
- Design, implement, and maintain large-scale data pipelines, ensuring efficient data flow and processing
- Work within a product team where solutions are collaboratively proposed. You will be expected to translate requirements into technical designs and implement them as part of large-scale data engineering solutions
- Collaborate with R&D, architects, data scientists, analysts, Project Managers, and DevOps to deliver robust and scalable solutions
- Analyze and explore datasets to understand business context, data meaning, relationships, and quality. Identify anomalies, data inconsistencies, and trends, and present actionable insights to technical and business stakeholders
- Leverage GenAI capabilities, AI agents, and Copilot-driven workflows throughout the software development lifecycle to improve productivity, code quality, design, testing, troubleshooting, and operational efficiency
- Create and maintain AI-powered skills, agents, prompts, and reusable development primitives that accelerate engineering processes and promote best practices across teams
Requirements
- At least 3 years of experience in Big Data technologies as a Data Engineer, including ETL/ELT processes, data exploration, data analysis, and advanced SQL queries
- At least 3 years of experience with Spark, Spark Streaming, Databricks, and other components required to build end-to-end data pipelines
- Deep knowledge of Big Data design principles and guidelines, data warehouse modeling, and data transformations
- Experience in cloud-based Big Data solutions and architectures (Azure OR AWS)
- Strong analytical skills with the ability to investigate datasets, understand data semantics, detect anomalies, validate data quality, and derive meaningful business insights
- Hands-on experience using Generative AI tools, AI agents, and LLM-powered development workflows, with a mindset of continuously adopting and creating AI-driven solutions to increase engineering effectiveness