Data Pipeline Development: Build, optimize, and maintain scalable data pipelines and ETL/ELT processes to support analytics and business intelligence needs.
Data Architecture Implementation: implement data architectures, including data lakes, warehouses, and integration layers that support the organization’s data strategy and modernization.
Data Quality & Governance: Implement data quality checks, validation processes, and monitoring systems to ensure data accuracy, consistency, and reliability.
Integration & API Development: Develop and maintain integrations between various data sources, third-party tools, and analytics platforms.
Collaboration: Work closely with Data & Analytics Managers and business stakeholders to understand requirements and deliver technical solutions that enable data-driven decision making.
Performance Optimization: Monitor and optimize data pipeline performance, query efficiency, and storage costs to ensure scalable and cost-effective solutions.
Documentation: Create and maintain comprehensive technical documentation for data architectures, pipelines, and processes.
Data Security: Implement security best practices and ensure compliance with data privacy regulations across all data systems.
Powered by AI: Leverage AI tools and automation to enhance data engineering workflows and productivity.