Introduction

Creating impact goes far beyond writing efficient code or building robust pipelines. It’s about understanding how your work translates into tangible value for stakeholders across the organization.



Types of Impact

Our work forms the backbone of data-driven decision making in organizations. However, measuring and communicating this impact isn’t always straightforward. If you feel your work isn’t making a meaningful difference, it might be time to pivot your focus or approach. Understanding the various ways we create value helps guide these decisions and ensures we’re contributing in ways that matter.


Measurable

The most straightforward way to demonstrate impact is through measurable outcomes. These are the concrete, quantifiable improvements that directly affect the bottom line. When you optimize a pipeline to run 50% faster, reduce storage costs by 30%, or improve query performance by an order of magnitude, these gains translate directly into business value through cost savings or improved efficiency.

These particularily have a impact in cloud spaces, as cost of infrastructure is so visable. These metrics are particularly valuable when communicating with leadership because they can be easily translated into financial terms. For instance, reducing compute time doesn’t just save on infrastructure costs – it might mean faster insights for the business or more timely data for customer-facing applications. These impacts compound over time, making them particularly powerful for demonstrating long-term value.


Hard to measure

Not all valuable work can be easily quantified. Consider the impact of improving data quality – while you might track the reduction in data incidents or SLA misses, the real value lies in the increased trust and reliability of your data systems. Building trust is a long plan game, it might have short term wins, but really doing this consistently over time will make your team “enjoy” work, as they can relax and clock off on time. However it will also make your customers have confidence in what you build and produce.

Mentoring and Coaching or improving documentation might not show immediate returns, but creates lasting value through team capability building and knowledge retention.

These improvements often manifest as the absence of problems rather than the presence of gains. When data pipelines run smoothly, documentation is clear, and teams can work efficiently with data, it might seem like business as usual. However, this reliability is the result of careful engineering work and attention to quality. While harder to measure, these impacts are crucial for long-term organizational success.


Immeasurable

Creating a thriving culture in a data team goes far beyond simple perks or surface-level engagement. It starts with understanding the fundamental drivers of human motivation: autonomy, mastery, and purpose. When data professionals have the freedom to make technical decisions and explore innovative solutions, they become more invested in their work. This autonomy, combined with opportunities to develop their skills through both challenging projects and manageable tasks, creates an environment where team members can truly flourish.

The foundation of a strong data team culture rests on psychological safety. When team members feel safe to experiment, take calculated risks, and learn from failures without fear of judgment, innovation naturally follows. This safety manifests in practical ways: open and constructive code reviews, transparent discussions about technical challenges, and honest retrospectives after incidents. Teams that embrace this mindset find that their members are more likely to share novel approaches, flag potential issues early, and collaborate more effectively on complex problems.

Measuring and nurturing this culture requires both structured approaches and organic growth. Regular learning sessions where team members share knowledge not only build technical capabilities but also strengthen bonds within the team. Rotating responsibilities ensures everyone gains a broader understanding of the system while preventing knowledge silos. Clear career development pathways show team members that their growth matters, while regular feedback sessions ensure everyone’s voice contributes to the team’s evolution. These practices create a virtuous cycle where improved culture leads to better outcomes, which in turn reinforces the positive environment.

The impact of a strong culture manifests in tangible ways: higher retention of talented team members, increased knowledge sharing, and more innovative solutions to technical challenges. But perhaps most importantly, it creates an environment where data professionals genuinely want to contribute their best work. When team members know their work matters, feel supported in their growth, and trust their colleagues, they’re more likely to tackle ambitious projects and push the boundaries of what’s possible. This combination of purpose, mastery, and psychological safety transforms a group of individual contributors into a cohesive, high-performing team that consistently delivers value to the organization.


The Long View on Impact:

Remember that impact often compounds over time. A well-designed data model might seem like a small improvement initially, but its value grows as more teams build upon it. Similarly, time invested in mentoring might not show immediate returns, but helps build a stronger, more capable team over time.

If you find yourself questioning your impact, consider:

  • Are you balancing short-term wins with long-term improvements?
  • Are you communicating your impact effectively to stakeholders?
  • Are you focusing on the right types of impact for your organization’s current needs?
  • Are you building foundations that enable others to create impact?


Translating Technical Work into Business Value

We often get caught up in the technical aspects of our work - pipeline optimization, data modeling, and system architecture. However, our true value lies in how these technical capabilities translate into business impact. Understanding and communicating this translation is crucial for career growth and organizational success.


Creating Value Through Data Quality and Trust:

The foundation of any data-driven organization is trust in its data. When stakeholders can rely on data for decision-making, they make better choices and act more quickly. Building this trust requires meticulous attention to data quality through rigorous validation checks, comprehensive documentation, and consistent engineering practices.


Efficiency and Cost Optimization:

While delivering insights often gets the spotlight, significant value comes from optimizing our data operations. This includes smart data modeling decisions, like knowing when sampling can provide directional insights instead of processing full datasets. It means building resilient pipelines that don’t wake engineers at 3 AM, contributing not just to cost savings but to team wellbeing and retention.


Enabling Self-Service and Scale:

One of the most powerful ways to create value is by enabling others to work more efficiently with data. This might mean building self-service tools for analysts, creating reusable data models for scientists, or implementing clear documentation that helps teams work independently. The goal is to move from being a bottleneck for data requests to being an enabler of data-driven decisions across the organization.


Beyond Just Providing Data:

To avoid burnout and create lasting impact, aim to balance different types of value creation. This might mean spending one quarter focusing on infrastructure improvements, another on enabling self-service capabilities, and another on direct insight delivery. The key is to maintain a portfolio of work that creates both immediate and long-term value.



Engineer Enablement

Being a force multiplier in data engineering extends far beyond individual contributions. While not everyone will become a Chapter Lead or Staff Engineer, you can amplify your impact across the organization through strategic enablement work. This means creating tools, frameworks, and processes that enhance the productivity and quality of work for entire teams.

Consider building reusable data pipelines that other teams can quickly adapt, creating testing frameworks that catch data quality issues early, or developing documentation templates that standardize knowledge sharing. For example, you might create a data validation library that automatically checks for common issues like null values, date formats, and business rule violations – saving countless hours of manual testing across teams.

Mentoring also plays a crucial role in enablement. By sharing your expertise through code reviews, lunch-and-learn sessions, or one-on-one mentoring, you help level up the entire data engineering organization. This could involve teaching best practices for data modeling, optimization techniques, or how to effectively troubleshoot pipeline issues.

Remember: true enablement isn’t about solving problems yourself – it’s about creating systems and tools that empower others to solve problems more effectively. This mindset shift from individual contributor to force multiplier is what distinguishes engineers who drive organizational impact.



The Art of Stakeholder Communication

As a Data Engineer, understanding and effectively communicating with your stakeholders is crucial. Your “customers” span across the organization, each with unique needs and ways of consuming data. Regular quarterly meetings with stakeholders help align data infrastructure with business needs.

Key Stakeholder Groups:

  • Business Intelligence Analysts
  • Data Scientists and ML Engineers
  • Product Managers and Product Owners
  • Executive Leadership
  • Marketing Teams
  • Operations Teams
  • Finance Department
  • Customer Support Teams
  • Sales Teams
  • Engineering Teams

Best Practices for Stakeholder Communication:

  • Schedule regular check-ins to understand changing needs
  • Create and maintain data dictionaries and documentation
  • Set up clear channels for data requests and issue reporting
  • Establish SLAs for data freshness and quality
  • Provide training sessions for new data tools or features
  • Communicate changes in data structure or availability proactively
  • Create feedback loops to continuously improve data quality
  • Maintain transparency about data limitations and issues
  • Build trust through consistent delivery and open communication


The Data Engineering “Last Mile”

Success in data engineering isn’t just about technical excellence - it’s about ensuring your work creates real value. This involves:

  • Awareness: Making sure stakeholders know what data exists and why it’s valuable
  • Persuasion: Demonstrating how the data will improve current processes
  • Usability: Creating intuitive interfaces and clear documentation
  • Expectations: Setting and meeting clear standards for data delivery


Conclusion

Creating impact as a data engineer requires a delicate balance of technical expertise, business acumen, and stakeholder management. By understanding the different types of impact you can have and tailoring your approach to various stakeholders, you can ensure your technical work translates into real business value. Remember that while some impacts are easily measured, others - though harder to quantify - can be equally or more valuable to your organization’s success.

The most successful data engineers are those who can bridge the gap between technical excellence and business value, creating systems that not only function efficiently but also drive meaningful organizational change. Whether through measurable improvements in pipeline efficiency or the more subtle impacts of cultural change, every aspect of your work as a data engineer has the potential to create lasting value.