About Exponentia.ai
Exponentia.ai is a fast-growing AI-first technology services company, partnering with enterprises to shape and accelerate their journey to AI maturity. With a presence across the
US, UK, UAE, India, and Singapore, we bring together deep domain knowledge, cloud-scale engineering, and cutting-edge artificial intelligence to help our clients transform into agile, insight-driven organizations.
We are proud partners with global technology leaders such as
Databricks, Microsoft, AWS, and Qlik, and have been consistently recognized for innovation, delivery excellence, and trusted advisories.
Awards & Recognitions
- Innovation Partner of the Year Databricks, 2024
- Digital Impact Award, UK 2024 (TMT Sector)
- Rising Star APJ Databricks Partner Awards 2023
- Qlik's Most Enabled Partner APAC
With a team of
450+ AI engineers, data scientists, and consultants, we are on a mission to redefine how work is done, by combining human intelligence with AI agents to deliver exponential outcomes.
Learn more: www.exponentia.ai
About The Role
We are seeking an experienced
Senior Data Engineer with strong hands-on expertise in building and delivering Databricks-based data solutions. The ideal candidate will design and develop high-quality data pipelines, optimize Spark workloads, participate in architectural decision-making, and guide the team in delivering scalable, reliable, and cost-efficient data platforms.
Key Responsibilities
Databricks & Data Engineering
- Design, develop, and optimize data pipelines using Databricks (SQL, Python, PySpark).
- Work with Delta Lake, Lakehouse architecture, and Unity Catalog for governance and lineage.
- Implement and manage Databricks workflows, cluster configurations, and job scheduling.
- Troubleshoot and optimize Spark jobs for performance, reliability, and cost efficiency.
- Integrate Databricks with cloud platforms (AWS/Azure/GCP), data lakes, and external systems.
Technical Ownership & Development
- Own the end-to-end design and delivery of complex engineering and data solutions.
- Build scalable ETL/ELT pipelines and collaborate with architects on solution design.
- Review code, enforce best practices, and maintain high engineering standards.
- Create reusable components, frameworks, and automation scripts.
Collaboration & Leadership
- Mentor junior/mid-level engineers on Databricks, PySpark, and data engineering best practices.
- Collaborate closely with product owners, architects, and cross-functional teams.
- Participate in sprint planning, design reviews, and architectural discussions.
Quality, Testing & DevOps
- Implement CI/CD pipelines for Databricks (Databricks Repos, GitHub/Azure DevOps/GitLab).
- Ensure high-quality deliverables through unit testing, data validation, and automated checks.
- Support production deployments, monitoring, and incident resolution.
