Essential Data Engineering Stakeholders: The Roles That Shape Your Work
Discover the important stakeholders data engineers engage with and why building strong relationships with them can shape your job.
Greetings, curious reader,
Starting a career in data engineering feels like stepping into a maze. You and I know the technical skills matter—coding, building pipelines, and managing databases. But there's another layer to the job: stakeholder management.
Newcomers often struggle to identify key collaborators. They face information overload and wonder how to balance technical tasks with communication.
Misaligned expectations can derail projects. Poor communication can stall progress.
Here's the good news. You and I can simplify this. By understanding key stakeholders, their needs, and how to collaborate, you'll set yourself up for success. This guide breaks it down step by step.
Why Stakeholder Management Matters
Stakeholders define the success of your data engineering projects. They're the people who rely on your work.
Stakeholders are the ones who decide if your pipelines deliver value. Ignoring them isn't an option.
Building strong relationships ensures smoother workflows. It reduces misunderstandings. It helps you prioritise tasks effectively. When stakeholders trust you, they become your advocates.
They support your projects. They push for resources when you need them.
Mastering stakeholder collaboration accelerates your career. It opens doors to leadership roles. It helps you transition into data architecture or analytics engineering.
You and I know technical skills are crucial. But soft skills are the secret sauce.
The Hidden Cost of Poor Stakeholder Management
What happens when stakeholder management fails? Projects drift off course. Requirements become unclear. Deadlines slip.
More importantly, trust erodes. Once lost, trust takes months or years to rebuild.
Consider this scenario: You build a technically sound pipeline. However, it doesn’t meet business needs.
Why? Because you missed essential context from key stakeholders.
The cost? Wasted time, resources, and damaged relationships.
Data Scientists and Machine Learning Engineers
What They Need From You
Data scientists and machine learning engineers rely on your work. They need clean, well-structured data for model training. They depend on reliable pipelines to access datasets on time.
They also need context. Metadata and documentation help them understand the data. Without it, they waste time guessing. They might even build flawed models.
When I started in data engineering, these were my first stakeholders. I quickly learned their needs shaped my priorities.
How You Can Help Them
You and I can design ETL pipelines that prioritise data quality. We can ensure datasets are accessible and well-documented. Automating data updates reduces manual work for them.
Collaborate on schema design. Align it with their analytical needs. For example, timestamp formats should match their requirements.
Missing or inconsistent data? Fix it before it reaches them.
Communicate pipeline changes in advance. Let them know about delays or issues. They'll appreciate the transparency.
Advanced Collaboration Strategies
Consider implementing feature stores for machine learning features. This reduces redundant work and ensures consistency across models.
Set up monitoring for data drift and quality metrics. This helps catch issues before they impact model performance.
Create sandbox environments where data scientists can experiment safely. This prevents accidental impacts on production systems while giving them the freedom to innovate.
Business Intelligence (BI) and Analytics Teams
What They Need From You
BI and analytics teams transform raw data into insights. They create dashboards and reports for decision-makers. They need aggregated, transformed data in formats they can query easily.
In companies with lower data maturity, you'll work with them often. They're the bridge between raw data and business value.
How You Can Help Them
Build scalable data infrastructure. Ensure it supports their reporting needs. Data warehouses or lakes should be optimized for querying.
Provide clear documentation. Explain data sources and transformations. Help them understand how to query complex datasets.
Collaborate on data consistency. Ensure metrics match across different BI tools. For example, revenue calculations should align in all reports.
Beyond Basic Support
Implement semantic layers to standardize business metrics. This reduces confusion and ensures consistency across reports.
Design data models that balance performance with flexibility. This helps BI teams adapt to changing business needs.
Consider creating self-service data platforms. This empowers BI teams while reducing your operational burden. Just ensure proper governance and documentation are in place.
Executive Leadership and Business Decision Makers
What They Need From You
Executives need high-level insights. They rely on data to guide strategic decisions. They want trustworthy data to support narratives and presentations.
You'll rarely work with them directly. Some of them might not even know you exist. But your work forms the foundation of their insights.
How You Can Help Them
Ensure data accuracy and reliability across systems. Collaborate with analytics teams to deliver actionable insights.
Simplify complex data. Present it in digestible formats. For example, use visualizations or summaries instead of raw numbers.
Don't talk about technology. Learn their language. Focus on business outcomes and storytelling.
Bonus: Software Development and Infrastructure Teams
What They Need From You
Software developers are not your stakeholders. You are their stakeholder. And you need to be good with them if you want them to be good with you.
These teams need clear specifications on data requirements. They want to know how much data you'll pull and how often.
They're technical people. They care about system performance and scalability. Business context? Not so much.
How You Can Help Them
Provide detailed documentation. Explain your data needs clearly. Share technical requirements upfront.
Collaborate on system architecture. Ensure it supports your pipelines. For example, discuss storage solutions or compute resources.
Communicate proactively. Let them know about changes or issues. They'll appreciate the heads-up.
Consider implementing Infrastructure as Code (IaC) practices together. This ensures consistency and reduces manual configuration errors.
Building Your Stakeholder Management Strategy
Assessment and Planning
Start by mapping your stakeholders. Understand their influence and interest levels. This helps prioritise your engagement efforts.
Create communication plans for each stakeholder group. Consider their preferred methods and frequency of updates. Remember, one size doesn't fit all.
Measuring Success
Track key metrics for stakeholder satisfaction. These might include:
Data quality metrics
Project delivery times
System reliability statistics
Also, ask for feedback. Have honest conversations. People rarely have these, and your stakeholders will appreciate it.
Continuous Improvement
Regular retrospectives help identify what's working and what isn't. Collect feedback systematically. Adjust your approach based on lessons learned.
Enjoyed this newsletter? Please show some love on LinkedIn or Bluesky or forward it to friends. It really helps!
💭 Final Thoughts
Data engineers act as bridges between technical and business worlds. You and I know the job isn't just about coding. It's about understanding needs and communicating effectively.
Strong stakeholder relationships lead to better project outcomes. They help you grow your career. They open doors to new opportunities.
Invest in both technical and soft skills. Learn to listen. Practice clear communication. Build trust with your stakeholders.
🏁 Summary
Data engineers must navigate relationships with several key stakeholder groups, each with distinct needs and expectations.
Data scientists and ML engineers depend on clean, accessible data for their models, while BI and analytics teams require structured data that enables clear reporting and insights.
At the executive level, leadership needs reliable, high-level data to drive strategic decisions. Software and infrastructure teams require precise technical requirements for system optimization.
Your success as a data engineer hinges on your ability to understand and respond to these stakeholder needs. This means not just delivering technical excellence, but doing so while maintaining clear communication and building trust through reliable delivery.
The most successful data engineers focus on delivering business value in everything they do. You can do it through implementing automated solutions, creating self-service capabilities, or maintaining strong technical partnerships across the organisation.
Ultimately, stakeholder management in data engineering is about finding the right balance between technical expertise and interpersonal skills.
A holistic approach to stakeholder management improves project outcomes and accelerates career growth.
Which stakeholder do you think will be your biggest challenge?
Until next time,
😍 How Am I Doing?
Your feedback shapes Data Gibberish. Which parts do you love? What would you like more of? Hit reply or use the links below—be honest.
Love how you emphasize trust as the foundation, once its lost, it’s a long climb back. But when managed well, those relationships create not just smoother workflows but real career opportunities too. Good one like always, Yordan.
Ooops, I thought it's Wednesday 😃