Here's how you can assess the key competencies of data architects during performance evaluations.

Experience and certifications are where you want to start. If someone is investing in gaining recognized certifications the the first checkpoint of aptitue in Data Engineering is crossed. Second is to give them use case based problems, ask them to build a system from scratch, keep evolving the problem by adding constraints like what if the space or compute is restricted or volumes can spike at specific times of the day. Lastly do check on future aspirations, do they wish to continue learning or have hit a plateau.

The best evidence for skill is to examine the outcomes of previous work. Theory craft and certifications are fine, but the question should be "Do I trust this person with my business?" What evidence do you have that the systems built achieved a measurable, desired outcome?

One book that really piqued my interest in this field is "Data Architecture: A Primer for the Data Scientist," complemented by courses from MIT OCW. These resources are excellent starting points for beginners. I recommend refreshing your foundational skills in DBMS and Python programming. It's also crucial to stay current with the latest big data trends and techniques. Certification is a key step, and of course, nothing beats hands-on practice.

In addition to these technical competencies, their ability to integrate these skills into a cohesive data strategy that aligns with organizational goals is vital. A technically skilled data architect not only ensures robust data architecture but also drives innovation and efficiency, ultimately contributing to the organization's success.

Technical skills and certifications are basics to start. Further to it, comes experience and use cases mastered in career path. Instead of very confined technical thinking, should put ourselves in end user shoes, thereby carving out an aligned precise bespoke product.

I firmly believe that strategic thinking is a crucial attribute for data architects. Their ability to align data strategies with business objectives and anticipate future data needs is paramount. Evaluating their approach to scalability, data security, and the integration of new data sources provides valuable insights into their strategic mindset. A proficient data architect offers clear justifications for their architectural decisions and comprehends how data flows within the organization drive long-term success. Their strategic vision not only ensures robust data architecture but also fosters innovation and positions the organization for sustained growth.

I think that data architects should be aligned with the company's strategy with which they interpret the company's new objectives. It is important that the strategic objectives are clear and driven by the data strategy.

Data architects need strong strategic thinking skills. Evaluate their ability to align data strategies with business goals and anticipate future data needs. Assess their approach to scalability, data security, and integrating new data sources. They should justify their architectural decisions and understand how data flows support long-term organizational success.

The assessment on thinking needs to be done on the use cases. The questions asked by the person and the solution to them. If the person is not asking questions then they are in the either end of the curve.

To assess the key competencies of data architects during performance evaluations, evaluate their strategic thinking. Determine how well they align data architecture initiatives with the organization's long-term goals. Review their ability to anticipate future data needs and challenges, and their capacity to develop forward-looking solutions. Assess their skills in creating comprehensive data strategies that drive business growth and innovation. By focusing on strategic thinking, you can identify data architects who contribute to the broader success and direction of the organization.

When assessing data architects, I emphasize practical data modeling exercises. It’s essential to observe their preliminary questions and their understanding of the business requirements. I evaluate their ability to create scalable and comprehensible models and consider how well they grasp the context and purpose of the project. Their approach to questioning existing structures and their flexibility in modeling are also key factors. For juniors, I focus on their intuitive understanding of data structures and their potential to learn. Practical skills are a decisive factor in my hiring process, as theoretical knowledge often doesn't translate into effective modeling capabilities.

Data modeling is essential for data architects, involving the structuring of data storage, organization, and access. Assess their proficiency in developing efficient and scalable conceptual, logical, and physical data models. Check for adherence to best practices in normalization and the ability to translate business requirements into data structures. Effective data architects present technically sound models that are also clear to stakeholders. A strong focus on these aspects ensures comprehensive and comprehensible data architecture.

The data modelling is strongly dependent on the technology and data. The modelling needs need to be evaluated. The use cases need to be asked in those ways. The modelling techniques for NoSQL DB to DBMS and Cloud DBMS need to be questioned. Use of different models need to be cleared. The fitment of the model needs to be done.

To assess the key competencies of data architects during performance evaluations, evaluate their data modeling skills. Examine their ability to design robust, scalable, and efficient data models that meet business requirements. Review their proficiency in using data modeling tools and techniques, as well as their understanding of normalization, denormalization, and schema design. Assess their capability to translate complex business processes into clear and effective data structures. By focusing on data modeling, you can ensure your data architects are effectively supporting the organization's data infrastructure needs.

Governance is an important part of the data. If we want to democratize the data and AI in the org, then we have to have knowledge on the governance. The assessment needs to be done on the usage of data, PIIs, masking, Implementation of the governance. The ways to implement it and testing the implementation. The rules of governance needs to be discussed. Do the rules create circular reference which will not enforce data governance etc.

To assess the key competencies of data architects during performance evaluations, evaluate their governance expertise. Examine their understanding and implementation of data governance policies, including data quality, security, and compliance standards. Review their ability to establish and enforce data management best practices and guidelines. Assess their proficiency in ensuring data integrity, privacy, and regulatory compliance across the organization. By focusing on governance expertise, you can ensure that your data architects are maintaining high standards for data management and contributing to a secure and compliant data environment.

I strongly advocate for the importance of evaluating a data architect's governance expertise. Effective data governance is critical for maintaining high data quality and ensuring proper data management. By assessing how the architect implements governance frameworks, adheres to compliance regulations, and handles data privacy concerns, we can gauge their dedication to upholding the organization's data integrity and security. Their ability to establish robust governance practices not only safeguards our data assets but also enhances our overall data strategy, ensuring long-term success and compliance.

Data governance is important for the definition and assurance of data governance, complying with standards that help to have better governance and control over the value of data. Important to have knowledge of data governance, data quality and governance team structure.

The communication is the second most important part after the technical skills. The BAs or the business will provide the business use case but its the DA whose job it will be to understand and create the overall solution. The future and present depends on this capacity. To assess this, we need to know the thought process of the DA. The ability to understand the problem and ways of raising concerns of volume and veracity in a friendlier manner.

To assess the key competencies of data architects during performance evaluations, evaluate their communication skills. Observe their ability to clearly articulate complex technical concepts to both technical and non-technical stakeholders. Assess their effectiveness in documenting data architectures, presenting ideas, and leading discussions. Review their proficiency in collaborating with cross-functional teams and their responsiveness to feedback and queries. By focusing on communication skills, you can ensure your data architects can effectively convey information and foster collaboration within the organization.

It is essential to learn new tools, concepts, frameworks, and technologies, however, it is even more important to possess the ability to apply these learnings to real-world applications and see them to fruition. Equally important is the capacity to unlearn outdated practices to pave the way for innovation and progress.