Data Engineer

• Ingest data from a variety of sources such as Azure SQL DB, Google Analytics, Google Play Store, Apple App Store, Salesforce, and others.
• Develop and optimize ETL/ELT pipelines to transform data from CSV, JSON, SQL tables, and APIs into usable formats.
• Work with REST APIs to pull data from various external sources and integrate it into our data ecosystem.
• Design and implement efficient data transformation processes to cleanse, aggregate, and enrich data.
• Apply industry best practices for data modeling to ensure scalability, performance, and data integrity.
• Collaborate with data analysts and data scientists to provide clean, high-quality datasets for reporting and analysis.
• Utilize Databricks for data processing, transformation, and orchestration tasks.
• Manage and optimize Databricks clusters for performance, reliability, and cost-effectiveness.
• Implement Databricks workflows to automate and streamline data pipelines.
• Use Unity Catalog for data governance and metadata management, ensuring compliance and data access control.

Data Engineer

ELK Data Engineer

Data Engineer – SME

Data Engineer – MTS

Data Engineering Manager, Big Data