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Job Analysis:
The Sr. Lead Analytical Data Engineer at ADP is fundamentally tasked with creating a robust data architecture that supports the development and enhancement of insights made available through the company's data analytics offerings. In this role, the individual is expected to bridge the technical and business sides of data, ensuring that data is accurate, accessible, and usable for reporting and analytics purposes. The responsibilities include engaging cross-functionally with various teams, documenting processes and best practices, and advocating for continuous improvement. The ideal candidate will need a deep technical background to handle complex data challenges, showcasing skills in ETL, data modeling, and governance, while also displaying leadership qualities in guiding a matrixed team towards achieving strategic objectives. Success in this role would look like delivering high-quality data products that drive significant business value and enable the analytics team to make informed decisions based on actionable insights.
Company Analysis:
ADP is a recognized leader in providing cloud-based human capital management solutions, positioned prominently in the HR outsourcing and analytics landscape. As a company that values innovation and service excellence, it creates a dynamic environment for roles such as the Sr. Lead Analytical Data Engineer. The corporate culture appears to be one that emphasizes collaboration, continuous learning, and a commitment to delivering value for clients and communities alike. Given ADP's focus on talent and team development, this role not only requires a strong technical acumen but also the ability to engage with various stakeholders to align analytical outputs with broader business goals. Being a part of a matrix organization means that the new hire will work closely with multiple teams and leaders, suggesting a need for exceptional interpersonal skills and a strategic mindset. This role is integral to ADP's goals of expanding its analytics capabilities—focusing on innovation while ensuring that the foundational data layer supports operational and client needs effectively.