Description

You will design and deliver modern data platforms using Databricks on AWS to enable trusted, governed, and scalable data consumption. You will establish a scalable data foundation that supports advanced analytics, AI/ML initiatives, and enterprise decision-making.

This role is on-site.

Responsibilities

• Lead the design and implementation of Databricks-based data platforms and scalable Lakehouse solutions on AWS.
• Architect and develop complex ETL/ELT pipelines using Apache Spark, PySpark, and cloud-native services.
• Drive MDM strategy, implementation, and integration across business applications and data platforms.
• Establish data governance, metadata management, and data quality frameworks to ensure compliance and consistency.
• Define data integration patterns using APIs, middleware, and event-driven architectures while mentoring engineering teams.

Required Skills

• 10+ years of experience in Data Engineering, Data Integration, or Data Platform delivery.
• Hands-on expertise with Databricks on AWS, including Apache Spark and PySpark.
• Strong experience with Delta Lake and Lakehouse architecture patterns.
• Proficient in AWS services: S3, Glue, Lambda, Redshift, IAM, and CloudWatch.
• Strong SQL and Python development skills for data processing and integration.
• Experience with MDM implementations and data governance processes.
• Experience integrating enterprise systems using middleware platforms (MuleSoft, Boomi, Kafka) or API-led integrations.
• Bachelor’s degree in Computer Science, Information Technology, Computer Engineering, or related IT discipline.

Preferred Skills

• Experience with structured, semi-structured, and unstructured datasets.
• Familiarity with CI/CD processes, Azure DevOps, Jira, and Confluence.

Key Skills

Education

Any Graduate