#036 - It's Not About the Money: Why Your Data Engineers Are Leaving
It's the daily battle against technical debt and broken tools that drains their motivation.
Hey there,
Your legacy systems are turning your data engineering team into a maintenance department.
I've seen this pattern destroy organisational momentum across enterprises:
Talented engineers hired to build competitive advantages
But spending 80% of their time keeping Teradata and Oracle systems operational
Business stakeholders waiting months for basic analytics improvements
As a technical leader, you're caught in an impossible situation:
Legacy systems demand constant attention
That attention prevents you from modernising them
Your best people get frustrated with maintenance work
They signed up to solve complex data challenges, not babysit servers.
Here's what 15 years of enterprise modernisation taught me: the organisations that break this cycle earliest gain an insurmountable advantage in talent retention and delivery speed.
In today's issue:
Why technical debt creates a talent retention crisis
The security exposure that legacy systems create in your organisation
The modernisation approach that improves team productivity while reducing operational risk
Let's examine why your current approach isn't sustainable...
If you're an IT leader watching your teams struggle with legacy systems that slow down every business initiative, here are the resources you need:
Weekly Resource List:
Breaking Technical Debt's Vicious Cycle - McKinsey (15 min read)
Strategic framework for executives on why technical debt compounds and governance structures are needed to break the cycleHow to Manage Technical Debt in 2025 - vFunction (12 min read)
Executive guide to architectural observability tools that help prioritise which systems need modernisation firstTechnical Debt Roadmap for Modernization - vFunction (10 min read)
Step-by-step approach to assess organisational readiness and create modernisation roadmaps that minimise disruptionData Modernization Strategy Guide 2025 - Athena Solutions (18 min read)
Comprehensive strategic overview of AI-powered modernisation and real-time processing trends for IT leadersTracking and Transforming Technical Debt - InformationWeek (12 min read)
Latest research on balancing debt remediation with innovation, including governance frameworks
4 Things Most IT Leaders Get Wrong About Technical Debt
(Even If You Think Modernisation Is Too Disruptive)
To achieve a sustained competitive advantage, it's crucial to understand why innovative IT leaders view modernisation as a strategic necessity rather than just an optional upgrade.
Here's what 15 years of enterprise transformations taught me:
Your Team's Productivity Is Being Systematically Undermined
The leadership challenge: Your most valuable technical talent is trapped in maintenance work instead of driving business innovation.
Across organisations I've worked with, there's a consistent pattern:
What I keep seeing:
Data engineering teams spend most of their time on system maintenance
Performance issues are consuming entire development cycles
Teams building workarounds instead of sustainable solutions
Innovation projects are constantly getting delayed for "urgent" fixes
Why is this pattern becoming more common?
Legacy systems require increasingly specialised knowledge
Each temporary fix adds more complexity to an already brittle system
The best technical talent gets trapped in maintenance roles instead of building new capabilities
What is this a concern: Talented engineers don't stay in maintenance roles.
They join organisations where they can:
Solve interesting problems with modern tools
Build capabilities that actually differentiate the business
Work with cutting-edge technology instead of legacy patches
Your technical debt isn't just consuming productivity; it's becoming a talent retention risk.
Legacy Infrastructure Creates Organisational Security Exposure
The hard truth: Your security posture degrades with aging systems, and patches can't address architectural vulnerabilities.
Security audits across enterprises are revealing a troubling industry trend:
What organisations are discovering:
Core systems operating on architectures that predate modern security frameworks.
Modern encryption standards are impossible without complete rewrites
Granular access controls would break existing integrations
Data access monitoring capabilities don't exist
The strategic risk isn't just known vulnerabilities:
Legacy systems can't adapt to evolving threat landscapes
Modern attack vectors exploit 15-year-old architectural assumptions
Incremental security improvements hit fundamental limitations
The industry shift: Modern platforms aren't just performance upgrades.
They're designed with security principles that legacy systems can't retrofit:
End-to-end encryption as a foundational capability
Zero-trust architectures built into the platform
Automated compliance monitoring that actually works
Security capabilities that are features, not add-ons
The Strategic Risk Paradox: Status Quo Is More Dangerous Than Modernising
While every IT leader fears modernisation risks, the true organisational threat is operational stagnation.
Most IT leaders share this concern: Why introduce migration risk when current systems are operational and functioning?
But organisations are finding that remaining in one place builds different kinds of risk.
The risks that compound over time:
Knowledge concentration risk: Fewer team members understand critical systems each quarter
Performance degradation risk: User expectations keep rising while systems stay static
Integration limitation risk: New business capabilities can't connect to the aging architecture
Recovery complexity risk: When failures occur, resolution becomes increasingly difficult
The industry insight: Modern cloud platforms actually reduce operational risk.
What modern platforms provide:
Less specialised maintenance requirements
Superior monitoring and observability capabilities
More reliable disaster recovery than legacy systems
Automated scaling and performance optimisation
The market reality: Staying with legacy is becoming the risky 'conservative' choice.
AI Initiatives Require Modern Data Infrastructure
Your AI strategy will fail if it's built on legacy data foundations.
Organisations across industries are discovering that AI initiatives on legacy infrastructure follow a consistent pattern: they don't work effectively.
Why legacy systems fail with AI:
Real-time machine learning requires real-time data access
Advanced analytics needs flexible data models
AI workloads demand elastic compute resources
Legacy systems provide none of these capabilities
Market leaders are demonstrating the competitive advantage that modern infrastructure enables:
Leading organisations are showing what's possible:
Modernised platforms supporting millions of connected devices
AI-powered predictive maintenance implemented at scale
Real-time decision-making across complex operations
AI implementation that feels natural instead of forced
The market reality: Organisations with modern data platforms rapidly iterate AI capabilities, while competitors with legacy systems struggle with basic machine learning.
This advantage compounds over time as AI becomes central to business differentiation.
The industry trend: Modern platforms turn AI from a complex integration challenge into a natural extension of data capabilities.
What market leaders are achieving:
Real-time fraud detection
Dynamic pricing optimisation
Personalised customer experiences
Predictive maintenance and optimisation
PS...If you're enjoying this newsletter, please consider referring this edition to a colleague. They'll get strategic insights for breaking free from the technical debt trap.
And whenever you are ready, there are three ways I can help you:
1. Data Modernisation Assessment
Comprehensive analysis of your legacy data systems with a practical migration roadmap that minimises risk and demonstrates clear business value
2. Current Workflow Audit
Deep-dive analysis of how your team actually spends their time on data systems, revealing hidden maintenance costs and productivity bottlenecks
3. Data Warehouse Modernisation
End-to-end transformation of your Teradata, Oracle, or legacy data warehouse to modern cloud platforms like Snowflake or BigQuery