Cloud Engineer

Position Summary

• We are seeking a hands-on Snowflake / Data & Application Engineer to support the development and modernization of data, reporting, analytics, and workflow capabilities within a commercial banking and credit environment.
• This role will work directly with business subject-matter experts and enterprise technology partners to translate business requirements, existing datasets, spreadsheets, reports, documents, and manual processes into working applications, analytics, automation, and reusable data products.
• The target environment uses Snowflake as a governed data foundation, with solutions potentially incorporating Streamlit, Python, SQL, Power BI, AI capabilities, and other enterprise technologies.
• Existing internal architecture also contemplates curated datasets, governed data products, semantic models, analytics, applications, and AI as different ways of consuming the same underlying data.
• This is primarily a hands-on builder role, rather than a strategy, project-management, or requirements-only position. The ideal individual can take an imperfectly defined business problem, understand the desired outcome, analyze the available data and existing process, and iteratively develop a practical solution.

Key Responsibilities
Snowflake & Data Development

• Develop, test, and maintain SQL supporting applications, reporting, analytics, and business workflows.

• Build and maintain Snowflake tables, views, transformations, mappings, and application-supporting data structures.

• Work with structured, semi-structured, and unstructured data.

• Translate source-system data into reusable, business-friendly datasets.

• Perform source-to-target reconciliation, record-count validation, field-level testing, and investigation of data discrepancies.

• Assist with data modeling, relationships, historical data, and business definitions.

• Document data lineage, source mappings, transformations, dependencies, and application usage.

• Develop solutions that can appropriately progress across development, testing, and production environments.

Application Development
Design, build, and enhance data-driven internal applications, particularly applications developed using Streamlit and Snowflake.
Solutions may include:

• Portfolio and management dashboards

• Data-entry interfaces

• Review and approval workflows

• Exception-management tools

• Operational workflow applications

• Administrative tools

• Tracking and assignment applications

• Data-ingestion utilities

• Data-validation interfaces

• Forecasting and analytical tools

• Document review applications

• AI-assisted workflows

The applications may need to combine traditional analytics with user input, data validation, workflow state, controlled write-back, and administrative capabilities. Existing development work specifically combines analytics with operational workflows rather than treating applications solely as read-only dashboards.
Python & Automation Development
Use Python to solve business and data challenges where SQL or traditional reporting tools alone are insufficient.
Examples may include:

• Data transformation and validation

• File processing

• Excel automation

• Document parsing

• Data reconciliation

• Application business logic

• Repetitive-process automation

• Integration utilities

• Analytical calculations

• Prototype AI workflows

The individual should be comfortable inheriting existing code, troubleshooting it, improving it, and determining when functionality should be refactored into reusable components.
Application Architecture & Maintainability
Help move rapidly developed prototypes toward scalable, supportable applications.
Responsibilities may include:

• Modular application design

• Reusable Python components

• Separation of UI, business logic, and data-access logic

• Configuration-driven development

• Session and application-state management

• Query optimization and caching

• Error handling and logging

• Role/access considerations

• Environment configuration

• Source control and code documentation

• Data-to-application lineage

• Performance and concurrency troubleshooting

An important objective is maintaining transparency between the application, its code, and its underlying data so that dependencies are understandable and applications can be modified or repointed without extensive reverse engineering. This need is reflected directly in current development discussions.
Reporting, Analytics & Visualization
Develop and enhance business intelligence and analytical capabilities using tools such as:

• Power BI

• Streamlit

• Snowflake

• SQL

• Python

• Excel / Power Query, where appropriate

The contractor should be able to understand an existing spreadsheet or report, identify its underlying calculations and business logic, and determine how that functionality can be recreated or improved using governed data and modern reporting/application technologies.
The broader internal data strategy emphasizes trusted, governed sources for leadership reporting, automation, analytics, and AI rather than creating isolated solutions for each use case.
Workflow & Process Modernization
Assist in converting processes currently dependent on spreadsheets, email, documents, or manual activities into structured applications and workflows.
Potential capabilities include:

• Structured data collection

• Review and approval routing

• Status tracking

• Assignment and ownership management

• Exception identification

• Business-rule validation

• Controlled overrides

• Historical record retention

• Notifications and action tracking

• Audit-friendly data capture

• Administrative/configuration capabilities

The developer should understand that the objective is often not simply to automate the current process exactly as it exists, but to identify the underlying business requirement and develop a more structured, maintainable approach.
Data Integration & Source-System Analysis
Work with business SMEs and technology partners to understand information originating from systems such as:

• nCino

• Salesforce

• Loan servicing platforms

• Credit and underwriting systems

• Financial-data providers

• Enterprise data repositories

• Excel-based models

• Document management platforms

• Other internal and external data sources

Existing work, for example, includes establishing broader Snowflake datasets from nCino/Salesforce information so that the data can support additional downstream use cases rather than only a single narrowly defined requirement.
The contractor does not need to be the enterprise integration owner. However, this person should understand how to analyze source data, identify keys and relationships, map fields, identify data-quality concerns, and work effectively with engineering teams responsible for enterprise pipelines and system integrations.
Unstructured Data & Document Automation
Support development of capabilities that convert files and documents into structured, usable information.
Potential sources may include:

• Excel workbooks

• Financial statements

• Operational reports

• Customer-submitted documents

• PDFs

• Images

• Supporting credit documentation

• Periodic reporting packages

• Other semi-structured or unstructured files

Current work has specifically identified unstructured-to-structured extraction as an opportunity, including extracting information from documents, validating results, populating standardized structures, and using the resulting information within downstream analytics and workflows.
Experience with document parsing, OCR/document intelligence, Python-based extraction, or AI-assisted document processing would therefore be valuable.
AI & Advanced Analytics
Exposure to Snowflake's AI capabilities or other enterprise AI technologies is preferred but not required.
Potential areas include:

• Snowflake Cortex

• Semantic models/views

• Natural-language analytics

• AI agents

• Document extraction

• Automated summarization

• Exception identification

• Reusable AI skills/workflows

• Human-in-the-loop AI applications

The target architecture already considers semantic views, natural-language querying, AI agents, governed data products and BI tools as complementary consumption mechanisms.
The emphasis should be on practical AI embedded within business processes, not AI experimentation for its own sake.

Required Qualifications
Snowflake / SQL
Strong working knowledge of:

• Snowflake

• Advanced SQL

• Relational data modeling

• Tables and views

• Data transformations

• Complex joins

• CTEs and window functions

• Data reconciliation and validation

• Query troubleshooting and optimization

• Development/testing/production concepts

• Access and security concepts

Python
Strong proficiency with Python, particularly for:

• Data manipulation

• Application development

• pandas or similar libraries

• File processing

• Automation

• Business logic

• Data validation

• Excel interaction

• Reusable/modular code

Application Development
Demonstrated ability to build meaningful business applications.
Experience with Streamlit is strongly preferred, including:

• Multi-page/modular applications

• Interactive forms

• Data validation

• Session/state management

• Database interaction

• Write-back functionality

• Interactive data visualization

• Application performance

• Reusable components

Data & Analytics
Strong understanding of:

• Data relationships and keys

• Data quality

• Source-to-target mapping

• Historical/trended data

• Business metrics

• Reporting logic

• Structured vs. unstructured information

• Troubleshooting inconsistent data

Reporting / BI
Experience with Power BI or a comparable enterprise BI platform strongly preferred.
Experience with Power Query, DAX, Excel, and related analytical tools is beneficial.

Preferred Experience
Banking / Financial Services
Experience in commercial banking, corporate banking, lending, credit, risk, or financial services is highly desirable.
Particularly valuable experience includes:

• Credit underwriting

• Portfolio management

• Loan monitoring

• Financial analysis

• Lending operations

• Collateral monitoring

• Covenant monitoring

• Risk management

• Structured lending/finance

Banking Technology
Exposure to one or more of the following would be beneficial:

• nCino

• Salesforce

• Commercial loan servicing platforms

• Credit underwriting systems

• Document management systems

• Financial market/data providers

Domain knowledge is a plus rather than a prerequisite. Strong technical candidates should be able to learn the relevant business concepts while working alongside business SMEs.
Development Tools & Technologies
Experience with some combination of the following is preferred:

• Snowflake

• Streamlit

• Python

• SQL

• Power BI

• Power Query

• DAX

• Git / version control

• VS Code

• Snowpark

• Snowflake Cortex

• REST APIs

• JSON / semi-structured data

• Excel automation

• AI/document-intelligence technologies

Candidates are not expected to possess deep expertise in every technology listed.

What Success Looks Like

• Turn business problems into working solutions
• The developer can work from a business objective rather than requiring a complete technical specification before beginning.
• Understand the data, not just the interface
• The developer traces the sources, relationships, definitions, calculations, and transformations behind what users ultimately see.
• Prototype quickly without creating technical debt
• Speed is important, but applications should remain understandable, modular, testable, and maintainable.
• Know the difference between prototype and production
• The individual understands where rapid business-led development is appropriate and where capabilities require formal engineering, security, governance, or enterprise integration support. That distinction has been an explicit consideration in the current Snowflake work.
• Diagnose problems independently
• When something doesn't reconcile or an application fails, the developer can work through the full chain:
• source data ? Snowflake ? transformation/business logic ? application/report ? user experience
• rather than troubleshooting only one layer.
• Build for reuse
• Common business requirements should become reusable datasets, components, functions, patterns, and frameworks where practical. The existing data strategy similarly emphasizes governed, reusable capabilities rather than duplicating work across use cases.
• Work effectively in ambiguity
• The individual is comfortable iterating alongside business users as requirements become clearer through working prototypes.
• Collaborate across business and technology teams
• The successful contractor can communicate comfortably with business SMEs, analysts, data engineers, architects, and technology partners and translate between those audiences.