Architect Advanced

Mphasis; New York (NY); On-site; USD 150,000 - 190,000; Full time

Job summary

Mphasis in New York, NY is seeking a Sr. Data Architect / Snowflake Data Modeler Lead Engineer to translate Finance requirements into Snowflake data models and semantic definitions.

You will lead hands-on implementation and collaborate with Finance stakeholders. The role requires 8-12 years in data warehousing, Snowflake, advanced SQL, and leadership in regulated environments; you will mentor engineers and contribute to CI/CD and production-readiness.

Qualifications

• 8-12 years of overall experience in data warehousing, data modelling, data engineering or business-intelligence platforms.
• Strong hands-on experience with Snowflake, advanced SQL and enterprise-scale analytical data models.
• Practical experience modelling Finance or regulated-industry data, preferably including GL, P&L, Revenue or Spend reporting.
• Experience designing dimensional models, transformation logic, reusable measures and data-quality controls.
• Experience leading engineering delivery, reviewing technical work and collaborating directly with business and data stakeholders.
• Proven ability to lead architecture discussions and work with business, data, security, governance and engineering stakeholders in a regulated environment.

Responsibilities

• Translate Finance requirements, business questions and reporting needs into logical, dimensional and physical Snowflake data models.
• Design fact, dimension, aggregate and consumption structures supporting Revenue, Spend and General Ledger use cases.
• Implement Snowflake equivalents for prioritized GL measures using reusable SQL and dynamic calculation patterns.
• Build and apply MTD, QTD, YTD and related period-based calculation utilities, using dynamic tables where appropriate.
• Develop source-to-target mappings, transformation rules, joins, hierarchies, conformed dimensions and data contracts.
• Collaborate with the Finance Semantic & Snowflake Data Architect to implement approved semantic definitions, ontology mappings and metadata requirements.
• Optimize Snowflake models and queries for performance, scalability, maintainability and cost-aware operation.
• Embed data-quality checks, reconciliation controls and traceability from source data to published Finance measures.
• Support unit, integration, regression, performance and user-acceptance testing with clear evidence and defect resolution.
• Provide technical leadership, conduct design and code reviews, and mentor Snowflake data engineers and SQL developers.
• Contribute to CI/CD pipelines, release controls, documentation, runbooks and production-readiness activities.

Skills

Advanced SQL

Data modeling

Leadership

Architecture reviews

Stakeholder collaboration

Education

Bachelor's or Master's degree

Tools

Snowflake

Job description

Experience:

Sr. Data Architect with more than 15 years of work experience.

Location:

New York, NY

Role Title – Snowflake Data Modeler / Lead Engineer

The Snowflake Data Modeler / Lead Engineer will work closely with Finance and data stakeholders to translate business requirements into detailed Snowflake data and semantic models. The role will lead hands-on implementation of Finance measures and modelling patterns supporting Revenue, Spend and General Ledger use cases, including Snowflake equivalents for the identified GL measures.

Job Functions/Duties And Responsibilities

• Translate Finance requirements, business questions and reporting needs into logical, dimensional and physical Snowflake data models.


• Design fact, dimension, aggregate and consumption structures supporting Revenue, Spend and General Ledger use cases.


• Implement Snowflake equivalents for prioritized GL measures using reusable SQL and dynamic calculation patterns.


• Build and apply MTD, QTD, YTD and related period-based calculation utilities, using dynamic tables where appropriate.


• Develop source-to-target mappings, transformation rules, joins, hierarchies, conformed dimensions and data contracts.


• Collaborate with the Finance Semantic & Snowflake Data Architect to implement approved semantic definitions, ontology mappings and metadata requirements.


• Optimize Snowflake models and queries for performance, scalability, maintainability and cost-aware operation.


• Embed data-quality checks, reconciliation controls and traceability from source data to published Finance measures.


• Support unit, integration, regression, performance and user-acceptance testing with clear evidence and defect resolution.


• Provide technical leadership, conduct design and code reviews, and mentor Snowflake data engineers and SQL developers.


• Contribute to CI/CD pipelines, release controls, documentation, runbooks and production-readiness activities.

Skills Required

8-12 years of overall experience in data warehousing, data modelling, data engineering or business-intelligence platforms.

Strong hands-on experience with Snowflake, advanced SQL and enterprise-scale analytical data models.

Practical experience modelling Finance or regulated-industry data, preferably including GL, P&L, Revenue or Spend reporting.

Experience designing dimensional models, transformation logic, reusable measures and data-quality controls.

Experience leading engineering delivery, reviewing technical work and collaborating directly with business and data stakeholders.

Proven ability to lead architecture discussions and work with business, data, security, governance and engineering stakeholders in a regulated environment.

Success Measures

• Finance AI use cases are grounded on governed Snowflake data, approved semantic definitions and appropriate access controls.


• Cortex Skills and agents meet agreed thresholds for accuracy, confidence, traceability, security and operational readiness.


• Reusable architecture and calculation patterns reduce one-off implementation and improve consistency across Finance use cases.


• Engineering teams receive clear, implementable designs and timely architectural decisions.


• Solutions meet agreed performance, reliability, data-quality, auditability and production-support expectations.

Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering or a related discipline.

SnowPro Core or Snowflake advanced certification is preferred.

Relevant cloud, data modelling, SQL or data-engineering certification is advantageous.