Job Description
Team: IT
This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Data Engineer – AI/BI in India.
This role is centered on building and scaling a modern data and AI platform powered by Databricks, enabling enterprise-wide analytics, AI-driven insights, and self-service BI capabilities. You will design robust data pipelines and governed data architectures based on Medallion principles to transform raw enterprise data into trusted, analytics-ready assets. The position involves working with large-scale integrations from systems such as PeopleSoft, Salesforce, and D2L, ensuring seamless data flow across the organization. You will also play a key role in enabling AI/BI experiences, including dashboards, semantic models, and natural language analytics through Databricks Genie. Operating in a highly technical and collaborative environment, you will contribute to building the backbone for data-driven decision-making and machine learning initiatives. This is a hands-on engineering role with strong impact across data, AI, and analytics ecosystems.
Accountabilities:
- Design, build, and optimize end-to-end data pipelines on Databricks using Apache Spark and Delta Lake, following Medallion Architecture (bronze, silver, gold) principles.
- Develop and operationalize AI/BI solutions including dashboards, semantic models, and Databricks Genie experiences for self-service analytics.
- Integrate enterprise data sources such as PeopleSoft, Salesforce, and D2L using APIs, JDBC, and other ingestion frameworks.
- Implement scalable ELT workflows with orchestration, monitoring, schema evolution, and automated error handling.
- Establish data quality frameworks, validation rules, and observability mechanisms to ensure accuracy, reliability, and performance.
- Implement data governance, lineage, and metadata management using tools such as Unity Catalog or equivalent solutions.
- Support AI/ML teams by delivering curated datasets, feature stores, and ML-ready data pipelines using tools like MLflow.
- Ensure data security, privacy, and compliance through access controls, encryption, masking, and governance best practices.
- Maintain technical documentation, architecture diagrams, and enablement materials for platform adoption and scalability.
- Strong hands-on experience with Databricks, Delta Lake, and Apache Spark in building scalable data engineering solutions.
- Proficiency in SQL and Python (or Scala) for data transformation, pipeline development, and workflow automation.
- Experience implementing Medallion Architecture and working with large-scale cloud data platforms.
- Proven ability to build and manage ELT pipelines, data ingestion frameworks, and orchestration workflows.
- Experience integrating enterprise systems such as Salesforce, PeopleSoft, or similar platforms.
- Understanding of data governance, metadata management, lineage tracking, and data quality best practices.
- Familiarity with cloud environments such as Azure or AWS, including storage and security concepts.
- Experience with BI/AI enablement tools such as Databricks AI/BI, dashboards, or semantic modeling is highly desirable.
- Strong analytical thinking, problem-solving skills, and ability to work in a fast-paced engineering environment.
- Remote-friendly work arrangement based in India with flexibility in working setup.
- Opportunity to work on cutting-edge Databricks AI/BI and enterprise-scale data platforms.
- Exposure to advanced data engineering, AI, and analytics technologies in a global environment.
- Strong focus on learning, technical growth, and hands-on innovation.
- Collaborative engineering culture focused on high-quality, scalable data systems.
- Opportunity to work on impactful enterprise data initiatives driving AI and business intelligence.
- Access to modern cloud and data stack tools and best engineering practices.
Requirements:
Benefits:
Explore More
Date Posted
05/19/2026
Views
0
Similar Jobs
Technical lead - Fullstack (.Net & ReactJS) - 3Pillar Global
Views in the last 30 days - 0
View Details