Data Engineer

Ready to shape the future of work? At Genpact, we don’t just adapt to change—we drive it. AI and digital innovation are redefining industries, and we’re leading the charge. Genpact’s AI Gigafactory, our industry-first accelerator, is an example of how we’re scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to agentic AI, our breakthrough solutions tackle companies’ most complex challenges. If you thrive in a fast-moving, tech-driven environment, love solving real-world problems, and want to be part of a team that’s shaping the future, this is your moment. Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions – we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation, our teams implement data, technology, and AI to create tomorrow, today. Get to know us at genpact.com and on LinkedIn, X, YouTube, and Facebook. Inviting applications for the role of Data Engineer In this role, a data engineer will lead the design and optimization of advanced data solutions. This role requires expertise in Databricks, Azure Data Factory (ADF), Python, PySpark and Unity Catalog to efficiently process and manage large datasets, along with a deep understanding of cloud architecture to build scalable, secure, and reliable data solutions on the Microsoft Azure platform. The engineers will drive innovation in data processing, ensure robust infrastructure, and collaborate with cross-functional teams to support strategic business initiatives. Additionally, the role involves mentoring junior team members and upholding best practices in data engineering to enable effective data-driven decision-making. Responsibilities Data Pipeline Development: Architect, build, and optimize data ingestion and transformation pipelines using Azure Data Factory and Azure Databricks, including PySpark and Delta Lake. Implement data integration and transformation solutions using Azure Databricks. Develop and deploy data models and solutions using Azure services. Pull/ingest data from different sources, transform and stitch, and wrangle it for advanced analytics activities. Data Quality and Governance: Ensure data quality frameworks, lineage, and monitoring are in place. Implement data quality checks, validation rules, and governance policies to ensure the accuracy, reliability, and security of data assets. Implement data security and privacy measures to protect sensitive information. Data Integration and Analytics: Pull data from different sources, transform and stitch it for advanced analytics activities. Design, implement, and deploy data loaders to load data into the engineering sandbox. Collaborate with data scientists and analysts to support their data requirements and prepare machine learning feature stores. Implement and manage Unity Catalog: Oversee data governance and security using Unity Catalog to ensure compliance and protect sensitive information. Leadership and Mentorship: Own complex, cross-functional data projects from ideation to production, including defining requirements, designing solutions, leading development, and ensuring successful deployment and long-term maintenance. Provide guidance and technical leadership to a team of data engineers through in-depth code reviews, mentoring junior and mid-level engineers, and fostering a culture of technical excellence. Mentor mid-level engineers and perform peer reviews. Leverage data best practices and tools and assist ML engineer in pulling, filtering, tagging, joining, parsing, and normalizing data sets for use Qualifications We Seek in You! Minimum Qualifications / Skills Bachelor’s degree in computer science, Information Systems, or a related field. 5+ years of experience in Databricks, Azure ADF, Python, Pyspark and Unity Catalog Proven experience as a Data Engineer with a strong background in Azure cloud services. Expertise in Azure Databricks, including its features for big data processing and collaborative notebooks. Strong programming skills in Python for data manipulation and scripting. Proficiency in SQL for data querying, analysis, and data modeling. Expertise in writing highly performant and fault-tolerant PySpark applications. This includes advanced knowledge of Spark's architecture, memory management, and performance tuning techniques. Deep knowledge of data governance frameworks and tools, including Databricks Unity Catalog, to ensure data security, quality, and compliance at an enterprise level. Expertise in setting up automated CI/CD pipelines for data solutions using Azure DevOps or GitHub. A strong understanding of MLOps is also crucial for building data foundations that support machine learning. Preferred Qualifications /