What do you do if your data engineering team is underperforming?

In my view, it is essential to resolve issues immediately to improve productivity, efficiency and overall performance. Some steps must be taken into consideration to improve performance, such as: Identify root causes, Clearly communicate performance expectations, goals and objectives to the team, Assess skills gaps in the team and provide training and development opportunities to address them , Hold team members accountable for their performance and results. Establish clear metrics and KPIs to measure progress and track performance over time and Lead by example and demonstrate a strong work ethic, commitment to quality, and willingness to collaborate and learn.

A good way to evaluate a data team’s performance is to: 1. Lay out all of the current tasks by project (in Jira or GitHub for example) 2. Include the status of each of the project tasks and have a person assigned to it 3. Include clear deadlines and inform your team of their changes as soon as possible

To determine whether a data engineering team is meeting its objectives, it's imperative to look at multiple factors like velocity, quality, and customer feedback. Once you have assessed the team on these criteria, the next step is to take a look at the expectations set at each role and task level and evaluate the performance of the team members based on the role and task they are assigned. This will give you insights into what are the issues where the team needs to do better as well as what areas need improvement, helping you build a plan to improve the team's performance. If the issue is with processes adopted by the team to gather requirements or design then those areas can be looked at to make improvements and help the team succeed.

Once the issues or areas of improvement are identified next step is to mitigate those issues by creating a plan and setting clear goals to achieve the recommended outcome. For example, if the goal is to reduce the data quality issues by 10% then the team would need to work on building or revising processes and standards around identifying and fixing data quality issues. This may need building a plan by working with the end customer to come up with a strategy for identifying the data quality issues and taking proactive actions to either fix or remove them.

Sometimes, there may be a skill gap in the team resources for example some of the team members may not be well-versed with the performance-tuning aspect of data pipelines. This can be improved by addressing the problem on multiple fronts. 1. Provide training to those team members to increase their skill level 2. Have standards and design practices in place for new joiners to follow to avoid making those mistakes in the future 3. Revise your hiring strategy to include those skills as required for new candidates to bring in engineers who are already skilled in that area.

Verifica como las comunicaciones estan realizando entre los miembros de tu equipo como entre el equipo de datos con el de negocio; tiempos, relevancia de la respuesta y pregunta, cantidad de documentos utilizados o cantidad de recurrencias del tema. Estos puntos pueden ayudar a evaluar como mejorar su comunicación.

Finally, you need to make sure to have KPIs or a feedback loop set up to understand how you and your team are performing on those goals and also whether the goals are being met or not. Based on this feedback or these KPIs you may need to alter or modify your action plan. Celebrating success along the way is also important to motivate the team and keep your team engaged and inspired.