Data Engineers Don’t Burn Out from Work They Burn Out from Pointless Work

Why Data Work Fails Without Purpose

Today’s data teams face more tools, dashboards, and expectations than ever. However, the real issue is not volume—it’s value. Much of the data work happening today delivers little impact. As a result, data engineers are burning out, not from effort, but from effort that leads nowhere.

In this blog, based on the Data Matas podcast, Co-op’s Head of Data Engineering, Nik Walker, reveals how to reduce burnout, cut cloud costs, and build trust in your data systems. His strategy centres on shifting from performative delivery to deliberate design—transforming how data work is done in large, complex organisations.

Rethinking How We Scale Data Work

Nik Walker leads Co-op’s data engineering team across retail, insurance, and funeral care. His approach emphasises people over pipelines and outcomes over optics. Rather than scaling for speed, his focus is on sustainable systems.

“Velocity doesn’t equal value. Safe teams deliver better data work.”

Filter Out Pointless Data Work

Most data engineers aren’t failing due to technical debt. Instead, they’re overwhelmed by low-value tasks. These include excessive dashboard requests, constant syncs, and undefined priorities.

Practical Steps:

• Audit tasks weekly: Categorise as business-as-usual, strategic, or reactive.
• Apply impact scoring: Evaluate based on urgency, business value, and technical effort.
• Empower refusal: Leaders must protect engineers’ time by making “no” a viable option.

As a result: More focused teams and fewer burnout cases.

Why Real-Time Isn’t Always Right-Time

Another source of wasted data work is unnecessary real-time syncing. Accordingly, Nik’s team uses a “right-time” model—only syncing data when business decisions demand it.

Practical Steps:

• Match syncs to decisions: If speed isn’t critical, avoid real-time.
• Switch to incremental loads: Reduces compute cost.
• Align with usage: Sync when data is actually accessed.

Consequently: Reduced cloud costs and less strain on engineering resources.

Build Trust Before Building AI

Effective AI begins with data teams trusting their own outputs. Therefore, Co-op ensures observability and quality before introducing any large language models.

Practical Steps:

• Log data origins: Make data lineage visible.
• Avoid blind trust: Include explainer tools and audit paths.
• Use shadow mode: Let AI run in the background until validated.

As a result: A data foundation that supports responsible AI use.

Discovery Must Precede Delivery

Skipping discovery leads to technical misfires and wasteful delivery. For this reason, Nik insists discovery phases are non-negotiable for effective data work.

Practical Steps:

• Block time for discovery: Include it in delivery cycles.
• Clarify the problem: Ask about the decision behind each request.
• Share discovery artefacts: Promote transparency and alignment.

With this in mind: Higher-quality output and reduced project rework.

Care is a Structure, Not Sentiment

Psychological safety at Co-op is built through structure and clarity. Unlike performative approaches, Nik’s leadership is grounded in well-defined systems.

Practical Steps:

• Standardise meetings: Define their purpose and owners.
• Support neurodiversity: Assume varied cognitive styles are the norm.
• Reward stability: Promote those who create calm environments.

Therefore: A culture where people do their best data work without fear.

A Four-Week Implementation Plan for Better Data Work

Week 1–2:
Audit workloads and tag activities by value.

Week 3:
Run a “stop doing” workshop and roll out impact scoring.

Week 4–6:
Optimise syncs and migrate to incremental processing.

Month 2:
Embed discovery sprints into all data initiatives.

Ongoing:
Operationalise psychological safety and clarity.

Metrics to Track:

• % of time spent on reactive data work
• Processing costs before and after optimisation
• Team satisfaction and burnout scores

Real Results from Co-op’s Data Work

By aligning sync schedules with actual usage, Co-op cut compute costs significantly. What’s more, stronger governance and support systems led to intentional delivery and happier teams.

The outcome? Engineers don’t dread Mondays—and projects no longer start in chaos.

Where to Start Improving Your Data Work

True transformation begins not with tools, but with how your team allocates time and energy.

To sum up:

• Focus on meaningful outcomes, not speed
• Align syncs to actual needs
• Build trust before using AI
• Create systems that support people, not drain them

🎙️ Listen to the full conversation with Nik Walker on the Data Matas podcast for more actionable guidance.

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Dig Deeper

🎙️ Listen to the full episode
 🔗 Connect with Nik Walker on LinkedIn
 🌍 Visit Matatika’s Website
 📺 Subscribe to Data Matas on YouTube

 

#Blog #AI Readiness #Burnout Prevention #Cloud Cost Optimisation #Data Engineering #Data Work