AI Data Reliability Engineer Intern | Financial Data Systems

About WalletGyde

WalletGyde is building a Financial AI platform designed to help people better understand, manage, and improve their financial lives through trustworthy, explainable, and personalized financial intelligence.

As our Financial AI architecture becomes more sophisticated, one principle has become increasingly important:

An AI system cannot produce trustworthy financial intelligence if the underlying financial data cannot be trusted.

WalletGyde integrates financial data through providers such as Plaid and transforms that information into normalized financial structures that support deterministic calculations, financial metrics, product experiences, and AI generated explanations.

We are now looking for an AI Data Reliability Engineer Intern to help strengthen the data foundation underneath that system.

This role is not simply about cleaning spreadsheets or correcting database records.

It is about helping engineer WalletGyde’s Financial Data Health layer.

The Role

As an AI Data Reliability Engineer Intern, you will work across financial data ingestion, APIs, normalization, schemas, transaction intelligence, categorization, validation, synthetic financial personas, and cross product data consistency.

The central question for this role is:

Can every downstream WalletGyde system trust the financial data it receives?

You will help us understand exactly how financial information moves from provider APIs into WalletGyde, how that information should be normalized and classified, and how we can systematically detect when something goes wrong.

You will also help us expand our synthetic financial testing environment from approximately 20 existing personas toward a much larger and more diverse library of controlled financial scenarios.

This role sits at the intersection of data engineering, FinTech APIs, AI systems, backend engineering, data quality, and Financial AI reliability.

What You Will Work OnPlaid Data Mapping

Help document and improve how WalletGyde uses financial data from Plaid.

You may work with data related to:

Accounts

Transactions

Balances

Income

Liabilities

Identity

Investments

Merchants

Recurring transactions

Transaction categories

You will help map provider data into consistent WalletGyde financial structures and document how individual fields are used throughout the platform.

Financial API Integration

Help strengthen the reliability of WalletGyde’s financial data ingestion workflows.

This may include working with:

REST APIs

JSON responses

Pagination

Date ranges

Transaction history windows

Refresh behavior

Error handling

Missing data

Sandbox environments

Response parsing

API field mapping

Rate limits and API reliability

The goal is to make sure WalletGyde receives the right financial information and handles incomplete or unexpected provider responses correctly.

Data Normalization

Financial institutions do not always represent information in exactly the same way.

You will help WalletGyde transform provider data into consistent internal structures that downstream systems can rely on.

This includes improving normalization for areas such as:

Merchant names

Account types

Transaction descriptions

Dates

Balances

Categories

Financial institution data

Income sources

Transfers

Debt payments

Schema Design & Data Contracts

Help review and improve the financial schemas supporting WalletGyde.

You may work with Supabase, PostgreSQL, and related backend systems to strengthen:

Table structures

Field definitions

Relationships

Validation rules

Data types

Financial taxonomies

Data contracts

Required versus optional fields

Historical financial data

The goal is to create a clear, reliable financial data model that can support both product development and AI evaluation.

Transaction Intelligence

One of the most important parts of this role will be improving how WalletGyde understands transactions.

Areas may include:

Merchant normalization

Transaction categorization

Transfer detection

Recurring transaction detection

Income classification

Credit card payment handling

Savings transfers

Investment transfers

Refunds

Business versus personal transactions

Mandatory versus discretionary spending

The challenge is not simply assigning labels.

Those classifications directly affect financial calculations such as income, expenses, cash flow, savings rate, debt ratios, budgeting, and financial recommendations.

Financial Taxonomy

Help e…