What do you do if your data engineering team is not driving organizational success?

Sometimes, data engineering teams are not aware of their pivotal role in driving overall organization's growth, which can make it challenging to stay motivated. Approach the issue as an overall alignment and help provide transparency to your teams. Offer visibility into how their work contributes to the organization's success. Consider a scenario where data engineering teams spent many hours to generate one report for a stakeholder. At times, this work might seem unacknowledged, yet it's essential in ensuring downstream users receive accurate and relevant data while streamlining processes for data analysts or product managers. Providing recognition and motivation in such cases can do wonders to your data engineering team.

One should identify the root causes by evaluating team dynamics, skill gaps, and alignment with strategic objectives. Foster open communication to identify challenges and areas for improvement, provide training or resources to address skill deficiencies, and redefine goals and KPIs to align with organizational priorities. Encourage collaboration across departments, promote a culture of innovation and accountability, and leverage data driven insights to inform decision making and drive impactful outcomes, ensuring the data engineering team contributes effectively to organizational success.

Instead of just ensuring the team's work aligns with organizational priorities, you need to be directly involved in scoping their efforts. Simply telling the data engineers to align with business goals is not enough – you must take an active role in shaping and defining what they work on. Too often, data engineering teams are left to make assumptions about what the business needs. This can lead to misaligned efforts that don't drive true organizational success. As a leader, you can't just hand them broad directives – you must roll up your sleeves and collaborate deeply with them on scoping initiatives.

align expectations and goals with the team, micro indicators that will help achieve results in the area and collaborate with the company's main objectives

Avalia??o e Alinhamento de Estratégias: Avalie a situa??o atual da equipe e identifique os problemas que est?o impedindo o sucesso. Reavalie a estratégia de dados da organiza??o para garantir que esteja alinhada com os objetivos organizacionais.

Outdated, inefficient data pipelines and ETL processes severely bottleneck an organization's ability to extract value from data. They create roadblocks that prevent the smooth flow of data insights to decision-makers. Legacy processes were built for a different era - they simply can't handle today's massive, real-time data volumes and varieties. Pushing modern data through these rusty, creaky workflows is like forcing water through a garden hose. It's not going to work. If the core pipes carrying your data are clogged and leaky, no amount of engineering talent will enable success. You must relentlessly optimize data engineering processes - automating steps, reducing handoffs, adopting newer technologies.

Maintaining an evolution policy is essential for the data area, every day new ways of creating an ETL emerge, reviewing time-consuming pipelines can bring great benefits and innovation

If my data engineering team isn't achieving organizational success, I'd start by identifying any issues and discussing them with the team. Together, we'd set clear goals aligned with company priorities and work on improving skills and capabilities. Through open communication and a focus on innovation, we'd strive to drive success within the organization.

Data engineering skills have a short half-life these days. The hot new tool today becomes legacy tech in a couple years. You have to continuously feed your team's cognitive toolkit with fresh skills and knowledge. Use it or lose it, as they say. Don't let your data brain trust atrophy into obsolescence. Invest in training programs, hack-weeks, bootcamps - whatever it takes to keep them sharp. Hire new blood with complementary skillsets. Because in this data-driven battle space, there's no prize for being the masters of irrelevant, outdated tech stacks.

Essential to remain competitive in this area and certainly the focus on continuous knowledge, this will keep the company increasingly ahead of others in this world of big data

It is important to foster collaboration within the data engineering team and across departments as it will help the team to create a direct or indirect impact on the organizational goals. Additionally, encourages data engineers to work closely with data scientists, analysts, and business stakeholders to ensure data infrastructure meets all the needs. Having said that, collaboration breaks down silos, promotes knowledge sharing, and aligns efforts towards common goals

Encourage collaboration and cross-functional teamwork between the data engineering team and other departments within the organization, such as data science, analytics, IT, and business units. Foster a culture of collaboration, knowledge sharing, and continuous improvement.

One of my best advice for every data engineer is to sit down with users and managers to really understand, what the data architecture needs to provide to them. Most often request get only passed down to engineers, so they simply build what they understand from this watered-down request, instead of talking directly to the people, who are using their infrastructure

Solicit feedback from stakeholders, team members, and other relevant parties to identify areas for improvement and opportunities for innovation. Foster a culture of continuous learning and improvement within the data engineering team.

Here is my experience and some of the techniques I use as a data engineer to adopt a mindset of rapid iteration: Implement agile methodologies, enabling the team to swiftly adapt to changes and innovate. Regularly reassess and adjust strategies based on feedback and performance metrics, ensuring alignment with organizational goals. Encourage experimentation with new technologies and approaches, fostering a culture of innovation and adaptability. Adopt a rapid iteration and continuous improvement mindset, viewing data projects as ongoing efforts to enhance capabilities. By staying agile and responsive, pivot your data engineering strategies as needed, keeping your team's efforts aligned with driving organizational success.

That's why smart teams are going all-in on modern DataOps practices centered around frequent iteration and continuous integration of changes. Tools like dbt have become indispensable for this methodology. dbt allows data engineers and analysts to modularize their data transformations and models as code. With version control, automated testing, and CI/CD pipelines, dbt enables truly agile development of data pipelines and datasets. Rather than dumping another multi-million dollar data mart on the business every 18 months, dbt lets you rap develop and release incremental enhancements on a weekly or even daily cadence. You can rapidly experiment, get feedback, and course correct based on shifting priorities and discovered needs.

?? What's wrong with us? When my data engineering team is not driving organizational success, it is necessary to take a proactive approach to identifying and addressing challenges. Let's get started with a thorough analysis of our practices, identifying gaps in people, infrastructure and processes. Implement corrective measures, from optimizing data architecture to reviewing workflows. Foster a culture of interdepartmental collaboration, integrating business needs into our technical initiatives. This strategic approach not only revitalizes teams, but also contributes significantly to organizational achievements, demonstrating the transformative power of strong data engineering.