Join NTT DATA as a Data Engineer Advisor, a pivotal role focused on the Databricks Lakehouse platform. You'll be instrumental in designing, constructing, and maintaining robust data pipelines and products. This position emphasizes data ingestion, transformation, governance, and optimization, all while upholding top-tier engineering standards. Collaborate with architects, analysts, and stakeholders to deliver exceptional data solutions that fuel analytics, reporting, and AI initiatives.

As a global leader in technology services, NTT DATA is committed to innovation and accelerating client success. We offer a dynamic and inclusive environment where you can grow your career. Join a forward-thinking organization dedicated to positively impacting society through responsible innovation and digital transformation.

Develop and manage efficient data pipelines within the Databricks environment, leveraging the latest processing capabilities. Design adaptable ingestion frameworks for both batch and streaming data, ensuring the creation of reliable, scalable, and reusable data products. Implement the medallion architecture pattern and build Databricks notebooks, workflows, and jobs with a focus on Delta Lake and Lakehouse best practices. Optimize performance and cost-effectiveness, while actively supporting platform monitoring and troubleshooting efforts.

Contribute to deployment automation using Terraform and CI/CD pipelines, managing code repositories and release processes. Enhance platform automation and drive operational improvements. Implement comprehensive data quality controls and support data lineage and governance requirements, ensuring adherence to security and data management standards. Produce thorough technical documentation for all developed components.

A minimum of 3 years of experience in Data Engineering is required, ideally with a Databricks engineering certification. Proven hands-on experience with Databricks is essential, alongside practical experience on AWS and with Infrastructure-as-Code tools like Terraform. Proficiency in modern ETL/ELT development methodologies is also a must.

Essential technical skills include Databricks, PySpark, SQL, Delta Lake, AWS, Terraform, Git, and CI/CD. Desirable skills encompass Unity Catalog, Kafka, Streaming Technologies, Data Quality Frameworks, and Machine Learning Pipelines. This role may involve hybrid work arrangements, subject to client requirements and business needs.