Core Purpose To design and maintain enterprise-wide data architecture that enables secure, scalable, and high-performing data solutions. This role ensures that data assets are structured, integrated, and optimized to support advanced analytics, AI/ML initiatives, and business intelligence. The Data Architect acts as a strategic enabler—defining standards, modeling frameworks, and governance practices—while partnering with stakeholders to deliver architectures that drive innovation, compliance, and operational excellence.
Key Responsibilities Data Architecture - Implement all aspects of data architecture, turning event analytics, raw application data, and business systems into key business insights.
- Develop conceptual, logical, and physical data models that align with business requirements and ensure scalability.
- Define and maintain data standards, naming conventions, and modeling best practices across systems.
- Integrate data from multiple sources, including transactional systems, streaming platforms, and third-party APIs, into unified architectures.
Collaboration and Knowledge Sharing - Work closely with product managers, business analysts, engineering teams and experts in the business to translate business needs into technical solutions.
- Develop and maintain comprehensive architecture diagrams, data dictionaries, and technical documentation.
- Provide training and guidance to teams on data architecture principles and best practices.
- Partner with AI accelerator teams to design innovative solutions leveraging AI/ML technologies and agent-based frameworks.
Solution Security, Scalability and Reliability - Work with Cybersecurity and Data Governance groups to design systems compliant with internal data standards, policies and security profiles, as well as privacy regulations such as GDPR and CCPA.
- Monitor and optimize database performance, query efficiency and cost management in cloud environments.
- Design architectures that support high availability, disaster recovery and future growth.
Skills & Qualifications Education & Experience - Bachelor’s degree in Computer Science, Information Systems, Data Science or related field
- 5-10 years of progressive experience in data-related roles
- Proven experience in designing and implementing data architecture (data warehouses, data lakes, lakehouses)
- 3+ years experience designing and implementing ETL/ELT pipelines
- 2+ years working with cloud-based data platforms (Azure, Databricks or MS Fabric preferred). Certifications in cloud architecture platforms a plus
Technical Fluency - Proficiency in Python and SQL
- Knowledge of data quality frameworks, metadata management and master data management.
- Familiarity with prompt engineering and agent-based tools (Copilot Studio, Azure AI Foundry, or similar)
- Knowledge of data visualization tools (Power BI, Tableau, or similar)
Core Competencies - Manages Complexity: Makes sense of complex, high quantity, and sometimes contradictory information to effectively solve problems. For example, asks questions to encourage others to think differently and enrich their analyses of complex situations. Accurately defines the key elements of complex, ambiguous situations.
- Cultivates Innovation: Creates new and better ways for the organization to be successful. For example, approaches professional work in unique or innovative ways. Offers original ideas. Appreciates others' creative ideas and solutions and provides helpful input to enhance them.
- Ensures Accountability: Holds self and others accountable to meet commitments. For example, tracks performance and strives to remain effective, learning from both successes and failures. Readily takes on challenges or difficult tasks and has reputation for delivering on commitments.
- Tech Savvy: Anticipates and adopts innovations in business-building digital and technology applications. For example, ensures that the team has adequate resources to invest in technology advancements and the training to use them well. Deploys some new technologies to enhance effectiveness of the group and business.
