You're a data engineer looking to transition to data architecture. What skills do you need to develop?

Data analysis is a must - To properly model data, you need to develop a deep understanding of understanding the activity a data point represents. To do this, you must be comfortable spending time and asking questions of the people who interact with the data daily. Finally, once you think you have a grasp on the data, find the edge cases, the things that go counter to what they described or defy logical intuition, and ask them how/why they occur. I don't like the term 'bad data' if it's produced as part of a business process. In most cases 'bad data' has a ton of value in understanding processes.

To transition from data engineering to data architecture, you'll need to enhance your skills in designing data systems, understanding data modeling techniques, and developing strategies for data governance and integration.

Data architects are responsible for designing the structure and organization of data systems, which requires an in-depth understanding of both conceptual and physical data models. You will need to develop skills in creating and optimizing data schemas, understanding entity-relationship diagrams (ERDs), and normalizing data for efficient storage and retrieval. Familiarity with data modeling tools, such as ERwin or Lucidchart, and proficiency in database technologies like SQL, NoSQL, and cloud data storage systems are also essential. Additionally, learning how to align data models with business requirements will be key to ensuring scalable and robust data architectures.

Data modelling is the foundation. Not insisting on "why", but on "how". As a must any Data Architect should have studied Kimball's and Inmon's books (last editions are revised to tackle also Big Data approaches). Actually this should be a mandatory reading for a mid level or senior Data Engineer (in my opinion).

As a data engineer looking to transition to a data architect role, I need to broaden my skills. Beyond my expertise in ETL processes, data modeling, and big data technologies, I should delve into data architecture principles like data governance, data security, and enterprise architecture frameworks. I'll focus on cloud platforms (AWS, Azure, GCP) and their architectural services. Improving my understanding of distributed systems, microservices, and API design is crucial. Additionally, I need to strengthen my communication and leadership skills to effectively collaborate with stakeholders and lead data strategy initiatives.

Prepare Data dictionary. It is helpful to provide a clear understanding about data across organizations. Data projects consist of data from multiple sources via files, databases, API, and Data lake which can be in on-premises and cloud. Adopting tools to manage these activities are essential. 1) Data Sourcing: - Document about the source, its native location, list of datasets it provides and each datasets field. 2) Data Classification: Classifying the data attributes which provide information about PII, security attributes, finance, etc; which helps to create data restrictions. 3) Data Lineage: It is essential to track the lineage of the data, where it comes from. It plays a key role in impact analysis

Transitioning from data engineering to data architecture requires developing a solid foundation in data governance, which involves understanding policies, standards, and practices that ensure data quality, privacy, and security across the entire lifecycle. Key skills include the ability to establish data governance frameworks, knowledge of compliance regulations like GDPR and CCPA, proficiency in data quality management, and experience in stakeholder engagement to ensure data is accurate, accessible, and used responsibly. Enhancing these skills ensures data assets are managed effectively and align with organizational goals.

Transitioning into data architecture would need enhancing skills in data modelling, data governance, data security and data quality. We would need to understand the data at enterprise level. Need to visualize the data flow across the systems, perform impact analysis on a change across the organization.

Data governance is the next brick layed on the foundation made by Data Modelling and Data Quality. Interlaces more with data modelling because proper access (by role) to data cannot be delivered without a proper data model. And starts with a proper data dictionary. Actually this is the most difficult chapter of data governance because starts with Business Data Dictionary and that means gathering and making business people have a common definition for the same busines term. And that is hard soft skill :) .

Data governance is crucial for ensuring data security, privacy, compliance, and ethics within an organization. As a data architect, it's vital to understand and implement the data governance framework and best practices. This includes designing data solutions that align with governance policies, processes, and roles. Effective communication and collaboration with data stakeholders, including business users, analysts, engineers, and regulators, are key to establishing data standards, rules, and definitions.

A Data Engineer who already knows data integration, data processing, data modeling, data infrastructure, and wants to transition to a Data Architect (DA) role, typically needs to: 1) Develop Strategic Thinking - Shift from focusing solely on implementation to thinking about high-level strategy, long-term planning, and how data systems align with business objectives. 2) Enhance Communication Skills - Improve the ability to sift out business requirements, communicate complex technical concepts to non-technical stakeholders, and collaborate with business leaders. 3) Obtain DA Certifications - Get validated via data architecture certifications like TOGAF Enterprise Architecture Practitioner or Certified Data Management Professional (CDMP).

To transition from data engineering to data architecture, enhancing your expertise in data strategy is crucial. This involves acquiring skills in aligning data initiatives with business objectives, understanding how to leverage data as a strategic asset, and planning for data storage, processing, and analysis to drive decision-making and innovation. Proficiency in identifying key data sources, evaluating new data technologies, and forecasting future data needs is essential. Developing a forward-looking data strategy also requires collaboration with stakeholders to support the organization's growth and competitive advantage.

Enhancing your grasp of broader data strategy will be your best bet in becoming a Data Architect. This is where you can learn the ins and outs of how you can align the complex data of an enterprise organization with the right set of tools for all the various use cases the company needs or wants to support. How does a data lake fit in the broader landscape? How does a data warehouse differ? How is data governed, and its access permissioned?

Utilizing a data strategy, an organization can pinpoint high-return data initiatives. As a data architect, align architectures with this strategy, considering business context and data landscape. Recommend suitable data technologies, platforms, and architectures for different scenarios.

Data strategy is the guiding vision and roadmap for leveraging data to achieve business objectives. It involves identifying data sources, needs, opportunities, and challenges, then prioritizing initiatives for maximum value. As a data architect, understanding the business context is crucial to designing architectures that align with the data strategy. This includes evaluating and recommending suitable data technologies, platforms, and architectures for various use cases and scenarios.

Data quality is crucial for ensuring that data is suitable for its intended purpose. It encompasses factors like accuracy, completeness, timeliness, consistency, and reliability, all of which impact data analysis and application performance. As a data architect, it's essential to design data solutions with high data quality standards and processes in place. This includes monitoring, troubleshooting, and implementing improvement and assurance methods to address data quality issues effectively.

Data quality is the second ingredient of the foundation, together with data modelling. Mandatory both in operational and analytical domains. While data quality on operational is comes as mandatory part of the features of operational apps, in the analytical field (data warehousing, business intelligence, AI, so on and so forth) it requires special attention on data integration time.

Transitioning to data architecture from data engineering necessitates a keen focus on data quality, requiring you to develop expertise in identifying, measuring, and improving the accuracy, completeness, reliability, and relevance of data. Mastery in implementing data quality frameworks, tools, and methodologies is essential, along with the ability to establish data quality metrics and standards. Understanding the impact of data quality on business decisions and analytics is crucial. This skill set ensures the integrity of data architecture and supports the trustworthiness of data-driven insights.

Data quality is the most important, yet the most difficult milestone to achieve. Data has to be trustworthy for the user, and this is only achieved if the data you provide is consistent, coherent, and reliable. Having different data layers, adapting the pipelines, and monitoring will help the team maintain good control of its data to ensure the best quality of what reaches the people who need to use it.

- To transition to data architecture, focus on developing skills in data modeling, data governance, data integration, and understanding of data storage solutions. - Strengthen your knowledge of data warehousing concepts, cloud architecture, and emerging technologies like big data frameworks and machine learning platforms. - Effective communication and stakeholder management skills are also crucial for collaborating with various teams and aligning data architecture with business goals.

Data integration is vital for creating a unified view of data from diverse sources and formats, enabling efficient data analysis and application usage. As a data architect, your role involves designing and implementing data integration solutions tailored to meet the requirements and specifications of data consumers. You'll utilize various data integration techniques like ETL (extract, transform, load), ELT (extract, load, transform), or data virtualization, along with tools such as Informatica, Talend, or Apache Airflow, to achieve seamless data integration across systems and platforms.

Learn how to explain technical jargon to business users in words they understand. It’s no good knowing how a solution could/should be built if you can’t explain how it will solve business problems.

To transition from data engineering to data architecture, it's crucial to develop skills in data modeling, system design, and architecture principles. Proficiency in data governance, understanding of business requirements, and the ability to translate them into scalable data solutions are also essential. Additionally, honing communication and stakeholder management skills is key.

As a Data Engineer, if you are aware of the tools and technologies to ingest, transform and curate data is sufficient. But as a Data Architect you should know end to end landscape of Data and business there by how we can solve the business problem with the help of Data by achieving Different tools and technologies associated with Data Modeling, Data Engineering, Data Governance and Visualization there by solving the business problem. I feel Data Architect should have technical and business knowledge to solve the future problems with Data by having full visio

In my perspective, there's a substantial overlap between the skills of a data architect and those of a data engineer. Enhancing a data engineer's skill set requires deep knowledge of DevOps processes for data pipelines, proficiency in cloud architecture, and a comprehensive understanding of data classification and governance. However, the cornerstone for a data architect's success lies in acquiring an in-depth understanding of the organization's data ecosystem and the ability to develop a comprehensive strategic vision.

Learn what data architecture really is. No, it’s not a tech stack. It’s the processes, tracking and communication between your technical team and the business stakeholders.

Transitioning to data architecture from data engineering 1. System Design: Architect scalable data solutions. 2. Data Modeling: Master conceptual and physical modeling. 3. Cloud Computing: Learn cloud-based data services. 4. Data Governance: Understand compliance and governance. 5. Business Strategy: Align architecture with business goals. 6. Security: Focus on data security and disaster recovery.

To transition from data engineer to data architect, one should - Understand the problem statement that you are going to solve - have prior experience in dealing data complexity - have good communication skill, interpretation skill, analytical skill and many more - know the various way to tweak the solution in case if needed. And offcourse below skills are needed, ?Design and solution ?Database knowledge ?Data modelling ?Data security ?Cloud knowledge ?Data governance ?Data management etc.

Understand the pain points of most data environments, have really good communication skills, specially asynchronous communication, be really good at creating design docs and explaining complex solutions.

Learn to manage up, down and across your level. This article talks about a lot of technical skills but doesn't touch on the fact that you will have to work with a lot of people in a lot of different roles and you will need to get them to to what you want them to do or give you what you need in order to continue. You will also need to sell your design to them and in order to do that you will have to understand their needs, speak their language and tailor the message accordingly. No amount of technical skills will save you here - you also need soft skills.

Come to terms with the idea that you don't know everything and cannot plan for everything. Sure, modeling techniques, nth abstract thought, documentation, governance and change management is important... But the most important thing, as a data architect, is the user. From an external customer all the way up to the CEO / President. Two of the most important questions you can ask are: what needs do you have that haven't been met and how do you see this evolving. Gathering unspoken requirements is of upmost importance.