Job Description
Job Title: Director Data Architecture
Overview:
We are seeking an experienced Director, Data Architecture to lead the evolution of our data platform, data architecture, and analytics capabilities. The role will define the data architecture roadmap, modernize the existing SQL-based data warehouse, improve data quality and reporting, and evaluate modern cloud platforms such as Azure and Snowflake.
The Director will lead a growing data team and partner closely with Finance, Operations, Service Delivery, IT, and Executive stakeholders to establish trusted, scalable, and accessible enterprise data solutions.
Roles & responsibility:
Data Architecture & Modernization
- Define and execute the enterprise data architecture roadmap.
- Lead the design, optimization, and scaling of the SQL-based data warehouse.
- Evaluate and recommend modern cloud data platforms, including Azure and Snowflake.
- Establish standards for data modelling, ETL/ELT, integration, governance, quality, and documentation.
- Architect integrations with systems such as Microsoft Dynamics and ServiceNow.
- Develop modernization strategies, technical recommendations, and implementation plans.
Business Partnership & Analytics
- Partner with Finance and business teams to improve reporting, KPIs, and data consistency.
- Establish common data definitions and enterprise reporting standards.
- Increase adoption of the data warehouse as a trusted source of enterprise reporting.
- Support self-service analytics and data literacy initiatives.
- Communicate technical concepts effectively to business and technical stakeholders.
Team Leadership
- Lead, mentor, and develop a data engineering and analytics team.
- Establish delivery standards, operating practices, and performance expectations.
- Prioritize initiatives and allocate resources based on business needs.
- Identify future capability and hiring requirements.
- Promote ownership, collaboration, accountability, and continuous improvement.
Execution & Delivery
- Oversee data initiatives from source-system ingestion through reporting.
- Ensure data platforms and pipelines are reliable, secure, scalable, and performant.
- Establish monitoring, testing, documentation, and operational practices.
- Identify opportunities to automate and improve data operations.
- Collaborate with IT, application, infrastructure, and business teams to resolve data issues.
Qualifications
- Over 15 years of overall industry experience, including at least 8 years in data engineering, data architecture, analytics engineering, or closely related disciplines.
- Experience designing, developing, and scaling enterprise data warehouses or data platforms.
- Experience evaluating and/or implementing modern data platforms such as Azure, Snowflake, or similar technologies.
- Experience integrating data from ERP systems such as Microsoft Dynamics and ITSM platforms such as ServiceNow.
- Demonstrated experience partnering with Finance teams on reporting, forecasting, KPIs, and business analytics.
- Experience leading, mentoring, and developing technical teams.
- Strong expertise in SQL-based data platforms, data modeling, ETL/ELT, and data integration.
- Strong analytical, problem-solving, communication, and project leadership skills.
- Willing to work during Eastern time
Preferred experience
- Experience with data platform modernization or cloud migration.
- Experience with Power BI or similar BI platforms.
- Experience in an MSP, professional services, technology services, or services-oriented organization.
- Knowledge of data governance, MDM, and data cataloging.
- Experience establishing enterprise reporting standards and business definitions.
Success Measures – First 12–18 Months
- Define the future-state data platform strategy and modernization roadmap.
- Increase adoption of the enterprise data warehouse.
- Standardize fragmented reporting, dashboards, and datasets.
- Improve data quality, reliability, documentation, and reporting confidence.
- Establish consistent enterprise KPIs and business definitions.
- Improve data platform performance, scalability, and maintainability.
- Develop a scalable data team structure aligned with business needs.
