Senior Databricks Data Engineer

About Saarthee: Saarthee is a Global Strategy, Analytics, Technology and AI consulting company, where our passion for helping others fuels our approach and our products and solutions. We are a onestop shop for all things data and analytics. Unlike other analytics consulting firms that are technology or platform specific, Saarthee’s holistic and tool agnostic approach is unique in the marketplace. Our Consulting Value Chain framework meets our customers where they are in their data journey. Our diverse and global team work with one objective in mind: Our Customers’ Success. At Saarthee, we are passionate about guiding organizations towards insights fueled success. That’s why we call ourselves Saarthee–inspired by the Sanskrit word ‘Saarthi’, which means charioteer, trusted guide, or companion. Cofounded in 2015 by Mrinal Prasad and Shikha Miglani, Saarthee already encompasses all the components of Data Analytics consulting. Saarthee is based out of Philadelphia, USA with office in UK and India Position Summary: We are looking for a highly skilled Senior Databricks Data Engineer with strong expertise in Databricks, PySpark, SQL, and AWS cloud technologies. This role involves designing and implementing scalable data architectures, developing robust batch and real-time data pipelines, managing data governance frameworks, and delivering high-performance data solutions. The ideal candidate will have hands-on experience with Databricks Lakehouse, Delta Lake, Unity Catalog, data modeling, and cloud-based data platforms while collaborating with stakeholders to translate business requirements into technical solutions. Responsibilities: Design and develop scalable batch and real-time data pipelines using Databricks, PySpark, and SQL Optimize SQL queries and PySpark workloads for performance and cost efficiency Implement data governance and access controls using Unity Catalog Build cloud-based data solutions leveraging AWS services Develop and maintain data models, warehousing solutions, and analytics datasets Collaborate with stakeholders to gather requirements and deliver technical solutions Drive best practices around code quality, CI/CD, testing, and version control Mentor team members and support continuous improvement initiatives Troubleshoot and resolve data platform and pipeline issues Qualifications: 6–8 years of Data Engineering experience Strong hands-on experience with Databricks, Delta Lake, and Unity Catalog Advanced proficiency in PySpark and SQL Experience with AWS cloud services (S3, IAM, Lambda, EC2, Redshift, etc.) Strong understanding of data warehousing, dimensional modeling, and ETL processes Experience building scalable batch and streaming data solutions Excellent communication and stakeholder management skills BE/B.Tech/M.Tech/MCA or equivalent qualification