7 Best Engineering Workflow Examples 

Mindaugas Gluchovskis ·

2019 - Present Marketing specialist Innovative content marketeer with MSc in International Communication, Mindaugas brings fresh ideas and inspiration about project management and beyond.

engineering workflow examples

Workflows are a holy grail for modern software development and engineering teams. As aptly written by Nickolas Means, VP of Engineering at Sym, in his article“The heartbeat of any software engineering team is the process they follow to move work from idea through execution to delivery.” Establishing a robust, process-driven workflow serves as a fundamental step for engineering teams of all sizes, providing a structured approach to move projects forward. 

In today’s technology landscape, engineering teams navigate a multitude of workflow methodologies, including AgileScrum FrameworkKanbanWaterfall, etc, to meet diverse project management goals.

Beyond this, software companies also integrate additional workflows into their processes. For example, code management, product development, and data engineering practices contribute to the seamless business functioning and delivery of the projects. 

In this article, we will explore some of the most effective engineering workflow examples, uncovering what sets them apart, and how organizations can leverage them at best. 

Learn more about engineering workflow stages here.

Top Engineering workflow examples

1. Agile workflow practices in engineering 

Agile, a profound methodology established over 22 years ago with the Manifesto for Agile software development, has become a rock-solid basis for software development companies and engineering teams. At its core, Agile is a project management approach that involves breaking down projects into iterative phases and prioritizing continuous collaboration and improvement.  

Agile standup board template
Agile board example

From a workflow perspective, engineering teams follow a structured Agile workflow: 

  1. Plan: This initial step involves the team’s comprehensive project planning and requirements gathering, setting the foundation for the entire project. 
  2. Design: With requirements in place, the team moves into the design phase, creating wireframes or prototypes that serve as the blueprint for the project. 
  3. Develop: Upon design approval, the team develops the prototype concept, translating the design into a functional product. 
  4. Test: Testing commences as the project advances beyond coding and development, ensuring the quality and functionality of the product. 
  5. Deploy: Following thorough testing, deployment takes place in the production environment, marking a crucial transition from development to the live environment. 
  6. Launch: The final stage involves launching the project, signaling its readiness for use and implementation. 
  7. Review: Iterative improvement is integral to Agile approach. Engineering teams regularly review and refine the project based on feedback to keep up with continuous enhancements. 

They do this not for the entire project but for each smaller project part. A comprehensive agile workflow contains the iterative and collaborative spirit of the Agile approach that allows engineering teams to continuously adapt, respond to changes, and deliver high-quality products. 

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2. Scrum-based engineering workflow 

Scrum is another popular agile framework for engineering workflows. Scrum-based workflows provide a flexible and iterative approach to project management. This approach organizes the process into time-boxed iterations known as sprints, typically lasting two to four weeks.  

Engineering workflow example

These are the critical steps involved in a Scrum-based engineering workflow: 

  1. Product Backlog: The process begins with creating a product backlog, a prioritized list of features, enhancements, and fixes. This backlog is dynamic and evolves as the project progresses. 
  2. Sprint Planning: Before each sprint, the team conducts a sprint planning session to select items from the product backlog to work on during the upcoming sprint. Those items are then placed in the Sprint backlog section on the Scrum board.
  3. Daily Stand-ups: Daily stand-up meetings are held for team members to share what they accomplished yesterday, what they plan to do today, and if they are facing any blockers. 
  4. Sprint Review: At the end of each sprint, a review meeting is conducted to showcase the completed work to stakeholders. This provides an opportunity for feedback and ensures that the project is on the right track. 
  5. Sprint Retrospective: Following the review, the team holds a retrospective to reflect on the sprint. This involves discussing what went well, what could be improved, and implementing changes for the next sprint. 
  6. Incremental Development: Scrum emphasizes delivering a potentially shippable product increment after each sprint. This incremental approach allows for the frequent release of valuable features and functionalities. 
scrum board
Scrum board example

Scrum-based workflow promotes collaboration, adaptability, and transparency, enabling engineering teams to respond quickly and deliver high-quality products. 

3. Kanban technique-based workflow  

Kanban, rooted in the lean development process pioneered by Toyota Motors, emerged as a prominent agile methodology. It offers a visual, less structured, and non-iterative approach. Unlike other methodologies, Kanban strongly emphasizes delivering continuous quality through efficient team collaboration. 

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Teamhood Kanban board

The Kanban engineering workflow involves the following steps: 

  1. Backlog: The starting point involves compiling and organizing a product backlog, listing all the tasks and requirements for the project. 
  2. Requirements: This stage focuses on refining and detailing the requirements essential for the successful execution of the project. 

Work in Progress (WIP) includes:

  1. Design: Once requirements are established, the team progresses to the design phase, where the overall structure and blueprint of the project take shape. 
  2. Development: With the design approved, the development stage commences as the team transforms the design into a tangible product or solution. 
  3. Testing: Quality assurance is paramount in Kanban, and the testing stage ensures that each increment of work meets the defined standards and requirements. 
  4. Deployment: Following successful testing, the product is deployed into the live environment, making it accessible to end-users. 
  5. Done: The final stage signifies the completion of a task or project increment, providing a clear visual cue that it has passed through all necessary stages and is ready for review or delivery. 

Kanban’s strength lies in ensuring an overall visual representation of the entire process. It enables engineering teams to respond swiftly and manage work continuously, making it a versatile and practical methodology for project management. 

4. Code management workflow 

Within engineering workflows, managing source code is a fundamental practice for engineers and software teams. Developers commonly employ tools such as Git and Mercurial to track and manage changes to codebases foster collaboration and code integrity. The source code serves as the backbone of software development pipelines and a practical code management workflow is essential to avoid bottlenecks or defects in the delivery pipeline. 

Git visual example (source)

Engineers adhere to best practices for source code changes, including regular commits, ensuring the repository is up-to-date, crafting detailed commit messages, reviewing changes, and implementing a proper workflow. A well-defined code management workflow outlines the following steps. 

  1. Create a New Branch: You create a new branch by using git branch [branch name] and git checkout -b [branch name]. 
  2. Make Changes: Write or modify code in the working directory. 
  3. Commit Changes: Stage changes with git add . and commit them with git commit -m by adding a descriptive message. 
  4. Push to Remote Repository: Push the branch to the remote repository using git push origin [branch name]. 
  5. Initiate a Pull Request: Create a pull request to propose changes. 
  6. Review Changes: Reviewers provide feedback on the pull request. 
  7. Address Changes: Make necessary changes based on feedback. 
  8. Approval: Upon approval, merge the pull request into the main branch. 

Engineering teams also use popular Git Workflows, like centralized workflows, feature branch workflow, gitflow workflow, forking workflow, and pull request workflow, which has its own set of steps and procedures, simplifying code management. 

5. DevOps pipeline workflow 

The DevOps pipeline facilitates seamless collaboration between development and operations teams in modern software development. DevOps aims to automate and optimize the software delivery process, fostering a culture of continuous integration, delivery, and deployment. The pipeline is a series of interconnected stages that enable the efficient and automated flow of code from development through testing to deployment.  

backlog software

The steps involved in a DevOps pipeline are as follows: 

  1. Code Commit: Developers commit their code changes to a version control system, such as Git. This initiates the pipeline and triggers subsequent stages. 
  2. Automated Build: Automated build processes compile the source code, run tests, and generate executable artifacts. This ensures code integrity and readiness for deployment. 
  3. Test: The pipeline automatically runs various tests, including unit tests, integration tests, and other quality assurance checks to identify and address any issues early in the process. 
  4. Deploy to Development Environment: Upon successful testing, the code is deployed to a development environment, providing a controlled space for further evaluation and collaboration. 
  5. User Acceptance Testing (UAT): The code undergoes UAT in a staging environment, mimicking the production environment. This phase allows stakeholders to validate the changes before moving to production. 
  6. Production Deployment: Approved changes are deployed to the production environment, ensuring a smooth transition from development to live usage. 
  7. Monitoring and Feedback: Continuous monitoring tools track the application’s performance, user behavior, and potential issues in the production environment. Feedback loops enable quick identification and resolution of any issues that arise. 
  8. Iterative Improvement: DevOps encourages a culture of continuous improvement. Teams analyze feedback, identify areas for enhancement, and iteratively refine the pipeline for future releases. 

The DevOps pipeline, emphasizing automation, collaboration, and continuous improvement, streamlines the software development lifecycle, enabling faster and more reliable delivery of high-quality software. 

6. Product management workflow 

Several SaaS (Software as a Service)-based companies encompass a systematic product management workflow to bridge the gap between creativity and delivery of a successful product. A well-defined product management workflow is vital for steering the product development lifecycle, aligning teams, and optimizing resources to deliver successful and market-responsive products.  

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The workflow involves critical steps to ensure effective product development: 

  1. Idea Generation: The workflow often begins with generating product ideas, which may arise from market research, customer feedback, or internal brainstorming sessions. 
  2. Conceptualization: Once an idea is identified, product managers work on conceptualizing its features, functionalities, and potential market impact. This involves creating a roadmap for the product’s development. 
  3. Prioritization: Product managers prioritize features based on market demand, business goals, and customer needs. This ensures that the team focuses on the product’s most valuable and impactful aspects. 
  4. Development Planning: Collaborating with engineering and development teams, product managers create a detailed plan outlining the technical requirements, timelines, and resource allocation for the product’s development. 
  5. Execution: Engineering teams implement the planned features and functionalities, guided by the product roadmap and development plan. 
  6. Testing: Rigorous testing is conducted to ensure the product meets quality standards. With various testing phases, such as unit testing and integration testing, product management also involves end-user testing. 
  7. Feedback and Iteration: Product managers gather feedback from stakeholders, users, and internal teams. This feedback informs iterative improvements and refinements to the product. 
  8. Launch: The product is launched with successful testing and iterations, making it available to the target market. 
  9. Monitoring and Maintenance: Post-launch, product managers monitor the product’s performance, user feedback, and potential issues, facilitating ongoing maintenance and updates. 
  10. End-of-Life (Optional): In cases where a product reaches the end of its life cycle, product managers oversee the retirement process, ensuring a smooth transition for users and stakeholders. 

This Product Management workflow ensures a structured and strategic approach to bringing products from ideation to market, fostering collaboration between various teams involved in the development process. 

7. Data engineering workflow 

As businesses collect vast amounts of data from different sources, transforming raw data into actionable insights becomes crucial. This is where data engineering plays a pivotal role in developing robust data analysis, business intelligence, and artificial intelligence applications. Hence, the data engineering team involves a systematic workflow to transform raw data into insights, ensuring the efficiency and reliability of data pipelines. 

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The data engineering workflow involves the following steps: 

  1. Data Collection: The workflow involves collecting data from various sources, such as databases, APIs, or streaming platforms. 
  2. Data Ingestion: Once collected, data is ingested into the system, where it undergoes initial processing to make it suitable for further analysis. 
  3. Data Processing: This stage involves cleaning, transforming, and enriching the data to ensure its quality and relevance. Standard tools like Apache Spark or Apache Flink are often employed for large-scale data processing. 
  4. Data Storage: Processed data is stored in data warehouses, databases, or data lakes, depending on the specific needs of the organization. 
  5. Data Modeling: Data engineers design data models that define the structure and relationships within the stored data, facilitating efficient querying and analysis. 
  6. Data Querying: Analysts and data scientists can then query the data using tools like SQL or specialized query languages to extract meaningful insights. 
  7. Data Transformation: Data is transformed into the desired format for reporting, visualization, or machine learning applications, aligning with the objectives of the analysis. 
  8. Data Loading: The final step involves loading the transformed data into the target systems, making it available for end-users or downstream applications to consume. 
  9. Monitoring and Optimization: Throughout the workflow, data engineers monitor the performance of data pipelines, identifying and resolving any issues. 

This comprehensive data engineering workflow ensures a structured approach to handling and processing data, enabling organizations to derive valuable insights for informed decision-making. 

Additional reading: Engineering productivity metrics leaders should monitor

Are you looking to streamline your engineering workflows?  

Seize the opportunity with Teamhood. Our solution offers unparalleled support for all your project management needs. Contact us today to elevate your software development and project management experience. 

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