GOALS

• Build and maintain a robust and scalable data pipeline to handle the ever-growing volume of customer and transactional data generated by the e-commerce platform
• Implement data quality checks and monitoring systems to ensure the accuracy and reliability of data used for business intelligence and decision-making
• Optimize the performance and efficiency of data ingestion, processing, and storage processes to reduce costs and improve the speed of data analysis

Frustrations

• Dealing with data inconsistencies and quality issues from various sources, requiring significant time and effort for data cleaning and preparation
• The complexity of managing and scaling the data infrastructure as the company grows rapidly, demanding constant upgrades and maintenance
• Lack of collaboration and communication between data engineering and data science teams leads to inefficiencies and misaligned priorities

GOALS

• Find a cost-effective and high-performance data warehousing solution that can handle the company's massive financial datasets and support complex analytical queries
• Improve the speed and efficiency of risk modeling and fraud detection algorithms by leveraging real-time data analysis capabilities
• Empower business analysts and decision-makers with self-service BI tools and dashboards that provide timely insights into market trends and financial performance

Frustrations

• High costs associated with traditional data warehousing solutions that struggle to scale with the company's growing data needs
• Legacy systems and outdated technology hinder the adoption of modern data analytics practices and limit the organization's agility
• Difficulty in finding and retaining skilled data professionals who can effectively manage and analyze large volumes of complex financial data