JOB SUMMARY

The Enterprise Systems Data Architect is responsible for defining and executing the firm’s data architecture strategy to enable trusted, integrated, and insight-driven data across the organization. This role will lead the design and implementation of an Enterprise Systems Data Governance Program encompassing client, engagement, and employee data, ensuring quality through sound data standards, and accountability throughout the data lifecycle.

The successful candidate will play a pivotal role in preparing the firm for AI enablement, establishing the foundation necessary for advanced analytics, automation, and responsible use of artificial intelligence. This position requires deep technical expertise and strong collaboration skills to support data driven strategic decision-making within the organization.

JOB DESCRIPTION

Enterprise Data Architecture

• Design and maintain the firm’s enterprise data architecture, including data models, data flows, and integrations across systems such as ERP, CRM, HR, Audit, Tax and reporting platforms

• Define and implement data standards, reference architectures, and best practices that ensure consistency, performance, and scalability across the firm’s data ecosystem

• Partner with technology teams and business leaders to develop a firmwide data strategy that supports analytics, operational efficiency, and innovation

• Partner with Integration and Operations teams to evaluate and implement modern data platforms, integration tools, and metadata management solutions to support cloud-based and hybrid architectures

Data Governance

• Lead the development and execution of the firm’s Enterprise Data Governance Program, focused on data lifecycle management, improving data ownership, quality, and accountability across client, engagement, and employee domains

• Define and maintain data stewardship roles, governance policies, and data management standards

• Maintain detailed documentation of data sources, methodologies, and analysis processes

• Develop and maintain Master Data definitions. Build consensus among system owners to define systems of record

• Develop processes for data quality, cleansing, metadata management, and master data management (MDM)

• Establish and enforce data quality standards, conducting comprehensive quality checks to ensure data accuracy

• Investigate and resolve data discrepancies, errors, and issues

Collaboration & Enablement

• Partner with business and functional leaders to align data initiatives with strategic firm priorities and client service goals

• Promote a data-driven culture through education, advocacy, and collaboration across departments

• Guide source systems Data Stewards on data standards and maintenance of data dictionary

• Act as a key advisor on AI readiness, helping business units identify where governed, high-quality data can accelerate insight, automation, and innovation

Technical Execution & Delivery

• Participate in data collection efforts, gathering and aggregating data from diverse sources, databases, and systems

• Ensure data accuracy, integrity, and consistency through meticulous data cleaning and validation processes

• Support data mapping and data transformation efforts, translating target system data requirements into mapping and transformation specifications

• Collaborate with the Development & Integrations team on the design and refinement of data warehouse architecture

Success Measures

In the first 12–18 months, success in this role will be measured by:

• Data Ownership: Clear definition and adoption of data ownership and stewardship roles across business units.

• Governance Framework: Establishment of a firmwide data governance framework with supporting policies, standards, and accountability structures.

• Data Quality & Cleansing: Implementation of data quality and cleansing initiatives that measurably improve data accuracy, completeness, and consistency.

• Reduction in Data Duplication: Consolidation and rationalization of redundant data sources through enhanced integration and MDM processes.

• AI Enablement Readiness: Foundational data architecture and governance structures in place to support AI and advanced analytics use cases.

• Stakeholder Engagement: Broad adoption of governance practices and collaboration across IT and business functions.

Supervisory responsibilities

• None

Work environment

• Work is conducted in a professional office