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 looking for a highly skilled
Data Architect with hands-on experience in modern cloud-based data platforms and strong working knowledge of Databricks. The candidate will architect scalable data ecosystems, design end-to-end data pipelines, and establish data standards to support advanced analytics, BI, and AI initiatives.
Key Responsibilities
Data Architecture & Platform Design
- Design and implement scalable enterprise data architectures across cloud environments.
- Develop conceptual, logical, and physical data models for analytical and operational use cases.
- Define data ingestion, transformation, and integration patterns using Databricks, Delta Lake, and related frameworks.
- Architect ELT/ETL pipelines leveraging Databricks Workflows, Delta Live Tables, or orchestration tools.
Databricks & Lakehouse Responsibilities
- Develop and optimize data pipelines on Databricks (SQL, Python, PySpark).
- Implement Lakehouse architecture principles using Delta Lake, Unity Catalog, and Databricks compute clusters.
- Optimize Spark jobs, cluster configurations, and cost/performance strategies.
- Work with Databricks features such as feature store, MLflow, Delta Sharing, and workspace governance.
Data Governance & Quality
- Define data quality rules, lineage, metadata standards, and governance frameworks.
- Collaborate with security teams to ensure compliance with data privacy and security requirements.
- Implement governance structures using Unity Catalog, RBAC, and data access policies.
Cross-functional Collaboration
- Partner with data engineers, analysts, AI/ML teams, and business stakeholders to deliver data-driven solutions.
- Translate business needs into scalable, secure, and efficient data architectures.
- Provide architectural guidance and best practices around Databricks and cloud data systems.
Strategy & Innovation
- Evaluate data technologies and recommend tooling aligned with modernization and scalability goals.
- Drive cloud migration and transformation initiatives, including legacy system modernization.
- Contribute to the long-term enterprise data architecture roadmap.
