Executive Summary
A financial institution needed to expand its ability to manage and utilize data across the organization.
Seneca helped design and build a multi-level data team—combining senior, mid-level, and junior talent—to support ongoing data initiatives and create a more sustainable approach to data management.
Results included:
- A structured, scalable data team
- Improved support for data-driven initiatives
- Increased flexibility in managing workload and priorities
The Challenge
As data demands grew, the organization faced a common problem: how to build a team capable of supporting both immediate needs and long-term initiatives.
Existing teams were often overloaded, with responsibilities spread across multiple roles. There was a need for:
- More specialized data expertise
- Better alignment between roles and responsibilities
- A structure that could scale as data needs evolved
At the same time, hiring remained challenging. Job descriptions alone didn’t fully capture what was needed, and finding candidates who could operate effectively within a complex, regulated environment required more than technical screening.
The Seneca Approach
Seneca worked closely with the client to design a data team that balanced experience, cost, and scalability.
Instead of focusing only on senior hires, the team was structured across multiple levels:
- Senior data professionals to lead initiatives and provide strategic direction
- Mid-level resources to execute and support core data functions
- Junior talent to handle foundational work and grow within the organization
This approach allowed the organization to build depth while maintaining flexibility.
Because of the long-term relationship, Seneca was able to:
- Interpret hiring needs beyond job descriptions
- Identify candidates aligned with both technical requirements and company culture
- Prepare candidates with context about the organization, improving interview success and onboarding
The Work in Practice
As the team grew, it became a central part of the organization’s data efforts.
The team supported:
- Data organization and cleanup initiatives
- Reporting and analytics projects
- Cross-functional data requests from multiple business units
- Early-stage AI and automation exploration
By distributing work across different levels of experience, the organization was able to:
- Allocate resources more effectively
- Reduce bottlenecks at the senior level
- Create a pipeline for developing internal talent
The Results
The result was a more sustainable and scalable approach to data.
Key outcomes included:
- A balanced data team aligned to both current and future needs
- Improved efficiency in handling data-related work
- Greater flexibility in managing priorities and workloads
- Stronger foundation for future data and AI initiatives
Key Takeaways for Leaders
- Building a data team requires more than hiring senior talent
- A tiered team structure improves scalability and efficiency
- Long-term partners can help interpret hiring needs more effectively
- Candidate preparation and cultural alignment improve hiring outcomes
