Senior Data Enginner

We are looking for an experienced Azure Databricks Data Engineer with strong hands-on expertise in Azure Cloud, Databricks, Azure Data Factory, Apache Spark, SQL, and Python. The ideal candidate will be responsible for developing, maintaining, and supporting scalable data engineering solutions while working closely with business and technical teams.
The candidate should also have hands-on experience with AI-powered coding assistants and be comfortable leveraging these tools to improve development productivity, code quality, debugging, and automation.
Responsibilities
Design, develop, and maintain scalable data engineering solutions using Azure Cloud, Databricks, and Azure Data Factory.
Develop, configure, schedule, and monitor Databricks Jobs to support business and data processing requirements.
Develop and maintain Databricks notebooks using Python/PySpark for data ingestion, transformation, validation, and processing.
Work with Databricks Unity Catalog for data access, governance, permissions, and data management.
Design and implement data pipelines using Azure Data Factory (ADF).
Develop efficient and optimized SQL queries for data extraction, transformation, validation, and analysis.
Use Apache Spark/PySpark for processing and transforming large volumes of data.
Develop automation and data-processing solutions using Python.
Monitor and troubleshoot Databricks jobs, ADF pipelines, notebooks, and production data issues.
Identify and resolve data quality, pipeline failures, performance, and operational issues.
Optimize Spark jobs, SQL queries, Databricks notebooks, and data pipelines for improved performance and scalability.
Collaborate with business stakeholders, data engineers, architects, and other technical teams to understand requirements and deliver effective solutions.
Participate in code reviews and follow established coding, testing, documentation, and deployment standards.
Use AI coding assistants such as GitHub Copilot or similar tools to accelerate development, generate and review code, debug issues, refactor code, and improve developer productivity.
Ensure solutions are secure, reliable, scalable, and aligned with Azure and data engineering best practices.
Support production deployments, incident resolution, and continuous improvement of data engineering solutions.
Maintain technical documentation for data pipelines, Databricks jobs, notebooks, and implemented solutions.
Requirements
Hands-on experience with Azure cloud
Experience with Databricks (experience in creating jobs, working with unity catalog, supporting business through notebooks)
Experience with Azure Data Factory
Hands-on experience in SQL
Experience with Apache Spark
Hands-on experience in Python
Hands-on experience with AI coding assistants
We offer
Opportunity to work on bleeding-edge projects
Work with a highly motivated and dedicated team
Competitive salary
Flexible schedule
Benefits package - medical insurance, sports
Corporate social events
Professional development opportunities
Well-equipped office
About Us
Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.

Work arrangement
Hybrid