Finance Unscripted Interview #2: On Reference Data, Rigor & the Hidden Backbone of Financial Markets

Meet Jude Sequeira — The Data Sentinel Steering Accuracy Across the Financial Trade Lifecycle

At Finacademics, we spotlight finance professionals whose work might be behind the scenes — but whose impact is front and center.

This edition features a seasoned expert in Reference Data — a Senior Manager with 16+ years of experience in financial services. With deep domain expertise in standardizing and optimizing data across multi-asset environments, they ensure that the information powering markets is accurate, timely, and trustworthy.

Here’s what happens when detail meets discipline.

First, tell us a little about yourself.

I am a driven and curious financial services professional with over 16 years of specialized experience in Financial Markets, particularly in the space of Reference Data. As a Senior Manager, I’ve led key initiatives to standardize, govern, and optimize reference data across complex, multi-asset environments. My expertise lies in ensuring data accuracy, integrity, and usability to support trading, risk, compliance, and operational efficiency. I’m deeply committed to building scalable data strategies that drive consistency and control across the enterprise.

1. What does “quality data” mean to you in practice — and how do you protect it?

To me, “quality data” means timely, accurate, complete, and consistent reference data that can be reliably used across the trade lifecycle — from front-office decision-making to downstream reporting and compliance. In the context of Financial Instrument Reference Data, quality is non-negotiable, as even minor discrepancies can lead to trade breaks, regulatory issues, or financial risk.

Protecting data quality starts with strong governance: clear ownership, defined data standards, and robust validation rules, ensuring controls are embedded throughout the data flow, from sourcing and enrichment to distribution. This includes proactive exception management, ongoing monitoring, and collaboration with both internal stakeholders and external data vendors. Data quality, in practice, is about enabling trust and transparency — at scale.

2. You’ve worked with large, complex datasets. What’s helped you turn data into something stakeholders can actually act on?

Working with large, complex datasets — especially in reference data — requires not just technical accuracy but also demands clarity and context. What’s helped me turn data into something stakeholders can act on is a deep understanding of how data flows end-to-end across functions and impacts the broader business. I focus on translating raw data into structured insights by aligning it with organization and stakeholder needs — whether it’s for front-office usage, risk management, or regulatory reporting.

I prioritize clear data lineage, intuitive visualizations, and domain-specific narratives to bridge the gap between data teams and decision-makers. Engaging stakeholders early and regularly also ensures that insights are actionable, relevant, and aligned with evolving priorities.

3. What’s one misconception people have about pricing or reference data that you often have to clear up?

This depends on who you’re engaging with. From a general standpoint, there are a few misconceptions that come up often:

• Reference Data doesn’t change much;
• It’s only relevant to operations or back-office.

People often think that once an instrument is set up, the data remains unchanged. In fact, reference data evolves regularly due to corporate actions, rating changes, regulatory updates, or product restructures. Staying on top of these changes and representing them accurately is vital to prevent downstream impacts.

As for the notion that it only matters to operations — in reality, front-office trading, risk, compliance, and finance all depend on accurate reference data. Misalignment in fields like instrument identifiers or classifications can directly lead to trade errors, compliance breaches, or reporting failures.

4. When data and judgment conflict — how do you navigate that moment?

Take a pause and investigate further.

Data is at the heart of everything, but its reliability depends on where the data has been collected from, its relevance, and how trustworthy the source is. Data validation is therefore important to identify quality issues, gaps, and inconsistencies. At the same time, it’s important to seek stakeholders’ perspectives — and often, the truth lies in reconciling data and our collective judgment.

In high-stakes environments like financial services, I believe in making decisions that are informed by both robust data and seasoned expertise — using one to challenge and refine the other, not override it.

“Good judgment doesn’t reject data; it questions it wisely. Good data doesn’t silence judgment; it sharpens it.”

5. If someone wanted to enter your field, what kind of curiosity or discipline would help them succeed?

To succeed in this field, a strong curiosity about how financial markets function — and how data flows through them — is essential. You should be the kind of person who is inquisitive and asks, “Why does this data point matter?” and “What happens if it’s wrong?”

This role rewards those who combine analytical rigor with cross-functional thinking — someone who can dive deep into data quality issues one moment and communicate effectively with trading, risk, or compliance teams the next. It’s not just about managing data; it’s about understanding the consequences of getting it right — or wrong.

“The best data strategy doesn’t start with a tool — it starts with a question.”

Curious for more insights from the minds behind the numbers?
Explore interviews, stories, and financial mysteries at Finacademics.com — where finance meets curiosity.