Data Architect

Job Description:
Are you ready to shape the future of data at a fast-growing, purpose-driven financial institution? In this dynamic role, you’ll be at the heart of innovation—designing enterprise data models, mapping source-to-target systems, and driving solutions that power smarter decisions across the business. You’ll own our corporate data architecture, champion data quality and governance, and steer initiatives in metadata, reference data, and master data management. If you thrive on big-picture thinking and precision execution, and you’re passionate about turning data into real-world impact, this is your moment. Help us build a smarter, more connected bank—where data fuels progress, and your ideas help lead the way.

Requirements:
1-3 Years of Data Architect Experience
-Ability to translate business requirements into scalable, efficient, and secure data architecture solutions

Strong SQL Skills
-Querying, transforming, and managing data across relational databases
-Optimizing SQL queries for performance and supporting data profiling, validation, and quality assurance

SSIS Experience
-Designing deploying and maintaining ELT workflows using SSIS
-Data Mapping and Source-to-target analysis experience

Erwin Data Modeling Experience – Highly Prefer this!
-Design, document, and manage enterprise data models

Python Experience – Highly prefer this!
-For data transformation, ETL orchestration, data validation, and automation

Nice to have:
Fabric

Responsibilities:
• Data Architects work on a broad range of data governance projects. Providing detailed data modeling and design for all data oriented development projects. Design logical and physical data models and maintain documentation. Conduct source to target analysis and mapping and maintain documentation. Create and update data dictionary. Participate in acceptance testing as required.
• Support implementation of reference data and master data programs. Define and maintain Match rules, Data cleansing rules, Hierarchies and affiliations, Golden records. Manage changes to reference and master data by identifying sources and contributors and establishing processes for managed change.
• Support the data quality program. Profile, analyze and assess data quality problems. Determine root cause of data quality issues. Define data quality metrics and business rules.
• Support metadata management policies and procedures. Develop, maintain and enforce standards. Manage metadata repositories. Create and manage widely accessible data dictionaries. Enforce policies and processes needed to improve and maintain the quality and ownership of data including data owners, stewards and administrators.
• Develop and monitor conformance with data standards, policies and procedures. Manage and resolve data related issues and conflicts. Monitor and ensure regulatory compliance. Establish and monitor documentation standards and repositories.
• Assist with defining the corporate data architecture and related standards. Data integration architecture. Data warehouse and business intelligence architecture. Taxonomies and namespaces. Data modeling tools and standards. Metadata standards and requirements.