Powering Up Modern Data Engineering Teams: Collaboration Best Practices

Organizations are investing in self-service capabilities to enable more associates to discover and analyze data, which creates new challenges for Data Engineers. Fundamental questions like "where is that dataset?" and "who owns it?" become more difficult to answer. Readily available modern tools enable Data Engineers to troubleshoot technical issues fairly quickly. However, now more than ever, Data Engineers need to be skilled at working collaboratively with other stakeholders, such as data scientists, business analysts, and executives, to address contextual issues promptly. In this blog, we explore common collaboration and communication issues between Producers (users who are curating datasets, such as Data Engineers in other domains) and Consumers (users who are analyzing data and/or building business intelligence solutions) and how to overcome their conflicting priorities and/or misunderstanding of the current state.

On the Producer's front, the tooling stack constantly changes, requiring intensive knowledge management to maximize its full benefits. Also, as the team grows, they are held to more rigorous practices around managing pipelines, including git, built-in testing, and even code reviews. Evolving standards and definitions require the producers to continuously align on effectively defining and maintaining their pipelines' quality. On the Consumer’s front, Data Engineers are expected to fully align with the business problem and context, including the various definitions of the data and what’s important to stakeholders. The common blocker for this is the long lead time to establish requirements and properly address the questions from these stakeholders, which can hinder solution delivery.

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Your team can do the following to address issues on both fronts: 

• Set a Clear Team Setup – roles and responsibilities – who are responsible for specific data sources and pipelines? How can you automate pipeline alerting and recovery? Who else needs to be informed?
• Leverage data flow diagrams/data walks – it’s crucial to contextualize the problem and flow of data with the business visually, so everyone understands how insights are generated.
• Implement DataOps & DevOps practices - the goal is to build more reliable pipelines over time to allow for a more seamless handoff and avoid troubleshooting old projects while starting new ones. The seven-step DataOps cookbook by Data Kitchen provides a good framework for getting started.
• Create a Knowledge Base – leaders to enable the experts within the organization to quickly share and govern the best practices for their tools, data, and analytics needs as the recipe for scaling.
• Continuously Improve - leaders should design and create communication channels for information to spread effectively across their organization. One way to do it is to establish communities of practice and share best practices across teams as well as hold project post-mortems and regular check-ins. Lessons learned are to be captured in the knowledge base (i.e when doing a code review, check for these areas).

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As we look to the future, the data and demand for insights will exponentially increase. Data Engineers will continue to face technical and non-technical challenges. The key to staying up to speed with the business is first to enable Data Engineers to interact and collaborate with business stakeholders early on. Second, enable Data Engineers to document and manage their knowledge in the flow of work, so it's both natural and efficient. AlignAI developed a platform that includes industry best practices and company-specific workflows to enable experts to capture their knowledge on how to build and use Data, Analytics, and AI products. Our platform serves as a single source of truth that pulls together resources on topics such as Data Ops and MLOps, empowering teams to collaborate and problem-solve more effectively. Schedule your demo today!

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