10 Tips for Building a Successful Data Engineering Team - Podcast Notes
Discover key tips for building an effective data engineering team.
Here are 10 key tips from the DataFramed Podcast Episode: Effective Data Engineering with Liya Aizenberg, Director of Data Engineering at Away Travel
Business Focus
Encourage an atmosphere where team members feel empowered to propose and test new ideas. However, ensuringthese ideas align with business objectives and drive tangible results is crucial. A balance between innovation and practicality is essential to avoid pursuing projects that don't contribute to the company's goals.
Key Traits for Early Hires
When building a data engineering team, look for individuals who are passionate about data and willing to learn. Strong foundational knowledge of data engineering principles is critical. Important traits include:
Passion for Data: Candidates should be enthusiastic about data and eager to explore new technologies and methods.
Technical Proficiency: Proficiency in SQL, Python, and BI tools is fundamental, as well as an understanding of relational and non-relational databases.
Adaptability: Adapting to new tools and technologies is essential in a fragmented industry.
Communication Skills: Effective communication ensures smooth collaboration within the team and across departments.
Focus: Employees should be dedicated to finishing what they start without getting distracted.
Building a Collaborative Culture
Promote a culture of trust and transparency where team members feel comfortable sharing knowledge and learning from each other. Encourage open-mindedness and a willingness to adapt to industry and company changes.
Selecting the Right Tech Stack
Choosing the right technology stack is crucial for the team's success. Focus on tools that are scalable, future-proof, and within the team's expertise. Avoid bringing in something new that does not align with the team's expertise, which can lead to inefficiencies and increased costs. Avoid the trap of buying too many tools, especially ones that overlap with each other.
Scaling the Team with Specialists
As the team grows, identify potential specialists within the existing team before hiring externally. Regular check-ins with team members are helpful for understanding their career aspirations and areas where they can be placed as specialists. If external specialists are needed, pair them with internal team members so both can learn from each other.
Effective Collaboration with Stakeholders
Building strong relationships across the organization is critical since organizations are made of people. Work closely with business teams in areas such as marketing, finance, and product to align data engineering projects with business objectives. Show non-data-savvy leaders the power of what is possible from a data standpoint. Get regular feedback through frequent check-ins with stakeholders to ensure the team does not get off track working on projects that do not provide business value.
Prioritizing Projects for Maximum Impact
Prioritize projects that address critical business needs and contribute to revenue generation or cost savings. Look for problems that are low-hanging fruit that can be addressed with minimal effort yet provide significant value. Attach a business metric to each project, such as website conversion rate, so the data engineering team can claim responsibility for ROI.
Agile in Data Engineering
Adopting agile methodologies helps the team stay flexible to frequent changes in business strategy impacting data engineering. Break down large projects into short sprints, starting with a minimum viable product (MVP), and fromthere, the team can prioritize additional features. Remember to work closely with the product manager as they are the bridge between tech and business.
Upskilling
Invest in your team's continuous development. Encourage team members to pursue training and certifications relevant to their roles. Assign projects to team members that will force them to learn new skills, but be sure they also align with their career goals for more intrinsic motivation.
Integrating Generative AI
It is important to experiment with Gen AI, although it can become a shiny object unless the business value can be proven. As Gen AI becomes more prevalent, data engineering teams will play a crucial role in integrating the large volumes of data required for AI models. Focus on developing skills in integrating data from many sources into one place.
Conclusion
Building and managing a successful data engineering team involves:
Balancing innovation with business needs.
Fostering a collaborative culture.
Prioritizing projects that drive value.
By focusing on these principles, data engineering leaders can create teams that are not only technically proficient but also aligned with the company's strategic goals.
Aizenberg, Liya. “Effective Data Engineering”. DataFramed by DataCamp. Podcast Audio.