An Enterprise Data Architect is a professional who specializes in designing and managing an organization's overall data architecture, ensuring that data assets are organized, integrated, and accessible across various systems and applications. These architects play a critical role in aligning data initiatives with business objectives and driving strategic decision-making processes. In this blog post, we describe the primary responsibilities and the most in-demand hard and soft skills for Enterprise Data Architects.

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Main Responsibilities of an Enterprise Data Architect

The following list describes the typical responsibilities of an Enterprise Data Architect:

Act as

Act as a liaison between technical and business teams on data-related matters.

Assess

• Assess existing data architecture and identify areas for improvement.

• Assess the effectiveness and accuracy of new data sources and data gathering techniques.

Co-create

Co-create the roadmap outlining how to evolve to the Target Data Architecture.

Collaborate with

• Collaborate and coordinate with multiple departments, stakeholders, partners, and external vendors.

• Collaborate with, and facilitates stakeholder groups, as part of formal or informal consultancy.

• Collaborate with application development teams to surface data solutions for all programs.

• Collaborate with cross-functional teams to deliver analytics solutions.

• Collaborate with IT and business leaders to prioritize data initiatives.

• Collaborate with peers to ensure requirements are understood and a high quality product results.

• Collaborate with stakeholders to understand data requirements and priorities.

• Collaborate with various stakeholders from business and IT during various phases of the project.

Communicate

Communicate information about data to users, application development, and database administration.

Complete

Complete a Data Architecture maturity assessment with recommendations for improvement.

Conduct

• Conduct data profiling and analysis to identify data quality issues.

• Conduct feasibility studies and cost-benefit analyses for data projects.

Coordinate

Coordinate change management of data models.

Create

• Create and maintain current- and target-state data architectures.

• Create data and analytics roadmap and build annual data and analytics technology plan.

• Create Enterprise Data Architecture standards and documentation.

Define

• Define and coordinate the enterprise data governance process.

• Define and maintain a data dictionary for the enterprise.

• Define and maintain KPI related to the enterprise data estate.

• Define data integration and migration strategies across systems and platforms.

• Define data lifecycle management policies and procedures.

• Define data standards, policies, and best practices for the organization.

• Define data storage and retrieval mechanisms for optimal performance.

• Define master data management strategies and processes.

Demand

Demand analysis, forecasting, supply and resource planning.

Design

• Design and maintain conceptual, logical, and physical data models.

• Design data visualization and reporting solutions.

• Design data warehousing and business intelligence solutions.

• Design mapping of data from source to target systems.

Develop

• Develop application data architecture models to further enable effective service-oriented delivery.

• Develop data architecture strategies aligned with business goals.

• Develop data transformation and ETL (Extract, Transform, Load) processes.

• Develop disaster recovery and business continuity plans for data assets.

• Develop processes and tools to monitor and analyze model performance and data accuracy.

• Develop proof of concept prototypes for next-generation data lake solution.

• Develop reference architecture and identify and document data flows.

Drive

• Drive engineering improvements and excellence within the team.

• Drive projects to completion.

• Drive the data culture across the enterprise.

Ensure

• Ensure Data lake design follows the prescribed reference architecture and framework.

• Ensure data security and compliance with regulatory requirements.

• Ensure technology solutions are in alignment with data architecture principles and target state.

• Ensure we have the right review process to select, design and implement the right data solutions.

Escalate

Escalate critical issues in a timely fashion.

Establish

• Establish and administer Data Quality rules to ensure appropriate quality.

• Establish data access and permissions controls.

• Establish data architecture documentation and knowledge sharing practices.

• Establish data governance frameworks and processes.

• Establish pilots and POCs for proposed solutions and make recommendations for approvals.

Evaluate

• Evaluate and recommend emerging technologies for data management, storage and analytics.

• Evaluate and select appropriate data management tools and technologies.

• Evaluate data models at all levels including conceptual, logical, and physical.

• Evaluate existing Data Management Policy, data modeling standards, guidelines and processes.

Facilitate

• Facilitate and ensure that common data operating model is established in delivery data domain.

• Facilitate data governance committees and working groups.

• Facilitate data-related training and workshops for staff members.

Give

Give direction in prioritizing effort.

Hold

Hold and participate in code reviews and design sessions.

Identify

• Identify and Analyze Cloud Infrastructure architecture gaps to propose new technologies.

• Identify any potential gaps and make recommendations accordingly.

• Identify data quality issue impacting the business and propose solution to address root cause.

• Identify, evaluates and recommend options, implementing if required.

Implement

• Implement data cataloging and metadata management solutions.

• Implement data quality management processes and procedures.

• Implement Meta Data Catalog with AWS tools or third-party tools.

Influence

Influence portfolio investments for key technology solutions in line with our roadmaps.

Lead

• Lead the data and data migration teams.

• Lead the design, development, implementation and maintenance of complex data systems and solutions.

Maintain

• Maintain information of processes that move data between data stores.

• Maintain information regarding identification of data stewards.

Make

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Manage

Manage and oversee metadata repositories and data workflows.

Meet

Meet with IT and business leadership to understand corporate, IT and Data and Analytics Strategies.

Mentor

Mentor junior team members in best practices and standards.

Monitor

• Monitor and identify appropriate tools and systems to achieve all data technology goals.

• Monitor and optimize data architecture performance and scalability.

Offer

Offer insight, guidance, and direction on the usage of emerging trends and technical capabilities.

Oversee

Oversee end-to-end data life cycle management activities.

Own

Own data governance related activities at the enterprise scale.

Participate

Participate in Agile ceremonies like standup, grooming and retrospectives.

Present

Present / communicate data models to the business, IT and project teams.

Produce

Produce accurate, unambiguous technical design specifications.

Provide

• Provide deep expertise for key technologies and platforms.

• Provide expertise to project teams for successful project implementation.

• Provide guidance and mentorship to data management teams.

• Provide hands on technical leadership and mentoring to the Data Engineers on the team.

• Provide technical leadership and direction on data architecture matters.

• Provide timely and accurate status reports as necessary.

Recommend

• Recommend data architecture best practices, guidelines, procedures and scalable frameworks.

• Recommend Improvements to optimize processes and deliver business value from our data.

• Recommend long-term direction on strategic advancements within the technical portfolio.

Review

• Review and analyze existing systems and make recommendations for improvements.

• Review Corporate Strategy, IT Strategy and Enterprise Data and Analytics Strategy.

Segregate

Segregate data models into ODS , data marts, and reporting layers.

Stay abreast of

Stay abreast of emerging trends and technologies in data management.

Support

• Support IT stakeholders across applications in implementing the data models and standards.

• Support solution architects to architect and deliver solutions.

Translate

Translate business needs into technology requirements and define data standards and principles.

Trigger

Trigger new initiatives related to data monetization or new insights / value creation.

Understand

Understand the data aspects of the project and design the model for current and future needs.

Work with

• Work with Data Management team to manage the data quality audits / rules and generate the reports.

• Work with little day to day supervision from project and / or line management.

• Work with Security team to categorize dataset by level of sensitivity and availability requirement.

Most In-demand Hard Skills

The following list describes the most required technical skills of an Enterprise Data Architect:

1. Proficiency in data modeling techniques and tools (e.g., ERwin, PowerDesigner).

2. Expertise in relational database management systems (e.g., Oracle, SQL Server).

3. Knowledge of NoSQL databases and distributed data systems (e.g., MongoDB, Cassandra).

4. Understanding of data warehousing concepts and architectures.

5. Experience with data integration tools and ETL processes (e.g., Informatica, Talend).

6. Familiarity with data governance frameworks and standards (e.g., DAMA, DMBOK).

7. Skill in data visualization and reporting tools (e.g., Tableau, Power BI).

8. Knowledge of cloud-based data platforms and services (e.g., AWS, Azure).

9. Proficiency in programming languages for data manipulation and analysis (e.g., SQL, Python).

10. Understanding of data security principles and encryption techniques.

11. Experience with data quality management tools and methodologies.

12. Knowledge of data architecture patterns and design principles.

13. Skill in performance tuning and optimization of data systems.

14. Experience with data migration and conversion strategies.

15. Proficiency in data cataloging and metadata management tools.

16. Understanding of data governance and compliance requirements (e.g., GDPR, HIPAA).

17. Familiarity with big data technologies and frameworks (e.g., Hadoop, Spark).

18. Ability to architect scalable and resilient data solutions.

19. Knowledge of machine learning and predictive analytics concepts.

20. Experience with data virtualization and federation technologies.

Most In-demand Soft Skills

The following list describes the most required soft skills of an Enterprise Data Architect:

1. Effective communication skills for translating technical concepts to non-technical stakeholders.

2. Collaboration and teamwork in cross-functional project environments.

3. Problem-solving abilities to address complex data challenges and issues.

4. Analytical thinking and attention to detail in data analysis and modeling.

5. Adaptability to evolving business requirements and technological landscapes.

6. Leadership and influence in driving data-driven decision-making processes.

7. Time management and organizational skills to manage multiple priorities.

8. Creativity and innovation in designing data architecture solutions.

9. Critical thinking and strategic planning in data architecture design.

10. Emotional intelligence and empathy in understanding user needs and concerns.

Conclusion

Enterprise Data Architects play a pivotal role in shaping the data landscape of organizations, enabling them to harness the power of data for strategic insights and competitive advantage.