GOALS

• Develop and automate data analysis reports and dashboards to provide timely insights into key business metrics and support data-driven decision-making
• Improve the accuracy and reliability of data analysis by implementing robust data quality checks and validation processes
• Collaborate effectively with data engineers and business stakeholders to understand data requirements, communicate findings, and drive actionable insights from data.

Frustrations

• Struggling with inconsistent data definitions and data quality issues across different systems, making it challenging to combine and analyze data from multiple sources
• Lacking the technical expertise to independently access, transform, and analyze large datasets, requiring reliance on data engineers and creating delays in obtaining necessary data
• Facing difficulties in effectively communicating complex data insights to non-technical stakeholders in a clear and understandable manner, hindering the translation of data into actionable business decisions.

GOALS

• Build and lead a high-performing data engineering team to support the organization's growing data needs and analytics initiatives
• Implement a robust data governance framework to ensure data quality, security, and compliance with industry regulations
• Optimize data infrastructure and processes to reduce costs, improve efficiency, and enable faster data-driven decision-making across the organization.

Frustrations

• Facing challenges in attracting and retaining top data engineering talent in a competitive job market
• Managing the increasing complexity of the data landscape, including data silos, disparate systems, and the need for real-time data integration
• Balancing the need for innovation and adoption of new technologies with the organization's existing infrastructure, security protocols, and risk appetite.