Key Responsibilities

• Design, develop, and optimize scalable ETL/ELT pipelines using Databricks and Apache Spark (PySpark/Scala).
• Build robust data ingestion workflows from structured and semi-structured sources.
• Develop reusable components and frameworks to streamline data processing and integration.
• Implement best practices for data quality, validation, and governance.
• Collaborate with data architects, analysts, and business stakeholders to understand requirements and translate them into data solutions.
• Tune Spark jobs for performance and scalability in cloud-based environments.
• Maintain and optimize data lake or Lakehouse architecture for high availability, security, and integrity.
• Support troubleshooting, debugging, and performance optimization in production workloads.
• Migrate on-premises applications to cloud service providers.
• Automate deployment and infrastructure using CI/CD pipelines (GitLab) and Terraform.
• Ensure adherence to coding standards and perform code reviews for quality and optimal execution.
• Partner with business and technical teams to implement best practices and achieve agile delivery goals

Must-Have Skills & Technical Expertise

• Databricks SaaS: Designing and building ETL/ELT pipelines.
• Apache Spark: PySpark and Scala programming.
• SQL & Python: Strong programming and querying skills.
• AWS Services: EC2, EMR, S3, EBS, RDS, DynamoDB, Glue, Identity & Access Management, Networking, Monitoring.
• Container Orchestration: Docker and Kubernetes.
• CI/CD & Deployment: GitLab pipelines, automation.
• Infrastructure as Code: Terraform.
• Data Architecture: Lakehouse, Starburst, Trino, federated queries.
• ETL / Data Pipelines: Development, optimization, and monitoring.

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