How I see it.
The past is prologue!
As a data analytics and data engineering professional, most learning happens in front of SQL editors,
dashboards, and documentation tools. Yet some of the most valuable lessons about systems, power,
and human behavior come from an unexpected place: historical drama.
One recent example is Versailles, a three-season series about King Louis XIV and the creation
of the Palace of Versailles. The show explores how a young monarch centralizes power by relocating
the French court from Paris to Versailles, transforming a hunting lodge into a tightly controlled
system of influence, politics, and authority. It is a world of shifting alliances, hidden agendas,
poisoners, and enemies operating at every level of status and power.
From Storytelling to Systems Thinking
Watching Versailles reframes the past—not as a simple sequence of events, but as a collection
of interconnected systems: political, social, and economic. These systems closely
resemble the complex data ecosystems encountered in modern work.
- The court at Versailles behaves like a high-dimensional dataset, full of noisy signals,
conflicting narratives, and missing information. - Power struggles mirror competing business priorities, where different stakeholders attempt
to shape the “truth” presented to leadership. - Every character, rumor, and decision represents data that must be interpreted, validated,
and placed in context before it can be trusted.
Just as data pipelines transform raw events into reliable insights, historical narratives
transform scattered actions into coherent stories that can be analyzed and learned from.
The Past as Prologue in a Data World
Shakespeare wrote, “The past is prologue,” a statement that fits naturally within a data-driven
mindset. History itself is a massive, unstructured dataset—fragmented, biased, and incomplete.
When examined thoughtfully, it becomes a powerful decision-support system.
- Data profiling resembles historical analysis: before trusting any source, its quality,
gaps, and limitations must be understood. - Data lineage echoes the tracing of causes and consequences in history—how upstream decisions
reshape downstream outcomes. - Documentation in data engineering serves the same role as historical records: preserving
context so future readers can interpret actions accurately.
In modern data and AI work, the same principles recur: observe carefully, question assumptions,
track dependencies, and design for long-term understanding rather than short-term wins.
Connecting History to Data Engineering Practice
Viewed through a data engineering lens, Versailles reveals familiar patterns:
- Centralizing the court at Versailles resembles building a centralized data platform,
consolidating fragmented sources of truth into a governed environment. - Managing nobles, factions, and rivalries parallels aligning business units around shared
metrics, governance rules, and definitions. - Hidden plots and misinformation reflect data quality issues—unverified numbers, duplicated
records, and misaligned KPIs that quietly undermine decision-making.
While the tools may be SQL queries, pipelines, and validation checks, the underlying goal remains
timeless: to create an environment where better, more reliable decisions are possible.
Why History Matters for a Data Professional
In a world increasingly shaped by data and AI, understanding how earlier societies navigated
uncertainty, conflict, and complexity is deeply valuable. History offers behavioral patterns,
governance lessons, and cautionary failures that translate remarkably well into modern technical
environments.
- It builds intuition for how complex systems evolve over time, especially as incentives and
power structures shift. - It reinforces the importance of governance—of people or data—when trust is fragile and stakes
are high. - It reminds us that behind every dataset and algorithm are human motivations, trade-offs,
and unintended consequences.
Reading history—and now watching it through series like Versailles—is more than
entertainment. It is a way to cultivate a more analytical, context-aware mindset that brings
together data engineering, governance, and human behavior to design clearer, more reliable
solutions for the future.
Software Engineer & Data Science| SQL, Analytics, and AI Solutions
Nuwan Hettiarachchi
I bring strong experience in data analytics and data engineering, with a focus on SQL-driven data preparation, data quality, and scalable processing pipelines. My background includes working with large, complex datasets, supporting business intelligence, and applying data governance principles such as profiling, lineage, and documentation. I am known for collaborating effectively across teams to design clear, reliable data solutions that support informed decision-making.
My Story
From Curiosity to Craft: My Journey in Technology and Analytics
My name is Nuwan Hettiarachchi, and my journey has been guided by curiosity, service, and a strong belief in using technology to create meaningful impact.
I began my professional path working closely with data, systems, and people. Early on, I realized that I enjoyed solving practical problems—especially those where analytical thinking and real-world needs intersect. This led me into data analytics, automation, and software development, where I’ve spent years building tools that improve accuracy, efficiency, and decision-making.
A defining part of my journey has been 10 years of volunteer teaching at a charitable organization. Teaching reinforced my belief that knowledge is most powerful when shared. It strengthened my communication skills, patience, and ability to break down complex ideas—skills that continue to shape how I design systems and collaborate with teams today.
Professionally, I’ve worked across data analysis, reporting, and application development. One notable experience was developing a Human Resources appraisal system over two years using Visual Basic and SQL Server, where I translated business rules into reliable, user-friendly software. Projects like this deepened my appreciation for clean data, thoughtful design, and systems that support people—not just processes.
Over time, my work expanded into Python, SQL databases, analytics, and automation, with a growing focus on data integrity and insight-driven solutions. I enjoy building tools that reduce manual effort, surface meaningful patterns, and enable better decisions.
Outside of work, I value balance and mindfulness. I enjoy hiking, traveling, kayaking, and spending time in nature—activities that keep me grounded and curious.
Today, I’m focused on contributing within data science and analytics–driven environments, continuing to learn, mentor, and build solutions that are practical, ethical, and impactful.
Technologies I’ve Worked With
Phone
(604) 256-2432
Surrey BC, Canada