If you’ve spent time in the world of data—whether as a leader or a practitioner—you’ve probably noticed that roles in this field can get fuzzy. Two roles that are often confused, conflated, or outright merged are the Data Engineer and the Data Platform Engineer. On the surface, they might seem interchangeable, but the nuances matter, especially when you’re managing teams or straddling both roles in one job.

Let’s break down the differences and similarities between these roles, and then tackle some advice on how to lead each role effectively. For those of you grinding it out in a combined role—don’t worry, we’ll get to you too.

The Key Differences

Data Engineer: The Builder of Pipelines

A data engineer’s bread and butter is enabling the movement, transformation, and integration of data. Their core responsibilities include:

• Designing, building, and maintaining data pipelines.
• Ensuring data quality and reliability through ETL/ELT processes.
• Working with stakeholders to understand data needs and delivering solutions.
• Optimizing storage solutions for performance and cost.

Think of data engineers as the folks laying down the tracks for a high-speed train. Their focus is on ensuring the data gets where it needs to go, clean and on time.

Data Platform Engineer: The Architect of Systems

A data platform engineer, on the other hand, focuses on building and maintaining the infrastructure that supports data operations. Key responsibilities include:

• Architecting scalable, secure, and reliable data platforms.
• Managing cloud services and on-prem environments for data storage and processing.
• Automating workflows and deploying infrastructure-as-code (IaC).
• Monitoring system performance and optimizing resource allocation.

They’re the ones building the train station—ensuring it’s ready to support trains of any size, at any time, without collapsing under the weight of peak traffic.

The Overlap

While their focuses differ, the two roles overlap significantly:

• Both prioritize reliability, scalability, and performance.
• Both require fluency in cloud platforms (AWS, Azure, GCP) and orchestration tools like Airflow or Dagster.
• Both rely on strong programming skills (Python, SQL, or even Scala/Java for those deep in the trenches).
• Both demand a keen understanding of business needs to ensure their technical work drives value.

In smaller organizations, it’s common to see one person wear both hats. In larger setups, specialization helps manage the complexity.

Leading Data Engineers and Data Platform Engineers

When you’re leading these roles, you need to adjust your approach to meet their unique focuses:

Leading Data Engineers:

1. Prioritize Delivery: Data engineers thrive on delivering tangible outputs. Break work into achievable milestones and celebrate those wins.
2. Think Frameworks: When tackling individual task try to look at the task in a broader sense.  Is there a way to build a framework that will allow for code re-use on future tasks that are similar through parameterization or a metadata driven approach?
3. Foster Stakeholder Communication: Encourage them to actively engage with analysts and data scientists. Understanding the “why” behind their work ensures alignment.
4. Tool Mastery: Invest in training and tools that make their lives easier, whether it’s dbt for transformations or Spark for big data.

Leading Data Platform Engineers:

1. Emphasize Long-Term Vision: These engineers are building for the future. Set clear strategic goals for scalability and resilience.
2. Encourage Experimentation: Let them test new tools and technologies. Platforms evolve rapidly, and staying ahead requires innovation.
3. Monitor Metrics: Uptime, latency, and cost-efficiency are their scorecard. Provide visibility into these metrics and reward improvements.

Managing Priorities When the Roles Are Combined

If you’re leading a team (or are the team) responsible for both roles, the challenge lies in balancing immediate needs with long-term investments. Here’s how to prioritize:

1. Triage Immediate Needs: Production issues and business-critical pipelines come first. They’re the lifeblood of operations.
2. Schedule Platform Improvements: Dedicate 20-30% of your capacity to platform enhancements. A strong foundation reduces future headaches.
3. Automate Ruthlessly: Whenever you find yourself repeating a task, automate it. From CI/CD pipelines to data validations, automation pays off.
4. Communicate Boundaries: Be clear with stakeholders about what’s achievable in a given timeframe. Transparency builds trust.
5. Document Everything: When juggling roles, good documentation ensures you don’t become a bottleneck.

Closing Thoughts

The data landscape is constantly shifting, and so are the roles within it. Whether you’re managing data engineers, data platform engineers, or both, the key is to understand their unique contributions and provide the support they need to excel. Build a culture of collaboration, prioritize ruthlessly, and never lose sight of the ultimate goal: delivering business value through data.

As a leader, your job isn’t to do the work for them but to clear the path so they can succeed. And when you’re wearing both hats? Remember to step back, breathe, and take pride in the systems you’re building—because they’re what make modern data-driven organizations tick.

What are your thoughts on this? Feel free to leave a comment and let me know if you agree/disagree or have anything to add.