Architect Premium III

Job Details: The Data Management team manages critical data assets, including the Operational Data Store, Loans Data Warehouse, Financial Sustainability Reporting System, Treasury and Asset Liability Management datasets, and related reporting and analytical solutions . Looking Senior Data Architect to lead the architecture and design of the Azure Databricks Lakehouse, establish a data-as-a-product framework, and support the continued management and modernization of the Loans Data Warehouse.

Role: Architect Premium III
Location: Washington, DC
Duration: 12 Months Long Term
Hybrid Onsite: 4 days per week from Day 1, with a full transition to 100% onsite anticipated soon.

Job Details:

• The Data Management team manages critical data assets, including the Operational Data Store, Loans Data Warehouse, Financial Sustainability Reporting System, Treasury and Asset Liability Management datasets, and related reporting and analytical solutions.
• As part of its data modernization program, client is establishing an enterprise data lakehouse on Microsoft Azure using Azure Databricks. The program will modernize legacy data platforms, strengthen governance, and deliver trusted and reusable data products for reporting, analytics, operational processes, and emerging AI use cases.
• Looking Senior Data Architect to lead the architecture and design of the Azure Databricks Lakehouse, establish a data-as-a-product framework, and support the continued management and modernization of the Loans Data Warehouse.

Scope of Work
The Senior Data Architect will perform the following activities:
Lakehouse Architecture

• Define and maintain the target-state architecture for the Azure Databricks Lakehouse.
• Establish scalable architecture patterns for data ingestion, storage, transformation, curation, publication, and consumption.
• Define integration patterns across source systems, Azure services, enterprise platforms, reporting tools, and downstream applications.
• Ensure alignment with enterprise architecture, cloud, security, data governance, performance, resilience, and cost-management standards.

Data Products

• Define the architecture, standards, and lifecycle for delivering data as a product.
• Establish requirements for data product ownership, business definitions, data contracts, metadata, lineage, quality, security, access, and service expectations.
• Design governed and reusable Loans and financial data products.
• Support publication and adoption of certified data products for reporting, analytics, operational, and AI use cases.

Loans Data Warehouse

• Provide architectural oversight for the Loans Data Warehouse, related data marts, interfaces, pipelines, and reporting datasets.
• Maintain and enhance Loans conceptual, logical, and physical data models.
• Assess existing data flows, transformation rules, dependencies, business rules, and downstream usage.
• Support resolution of complex data, integration, performance, and reporting issues.
• Define the coexistence and transition architecture between the Loans Data Warehouse and the Azure Databricks Lakehouse.

Data Modeling and Integration

• Develop conceptual, logical, and physical data models for priority financial and operational data domains.
• Define reusable models for transactional, master, reference, historical, and analytical data.
• Produce source-to-target mappings and document transformation and integration rules.
• Establish patterns for batch and incremental processing, historical loading, error handling, monitoring, and recovery.
• Review data models and technical designs prepared by implementation teams.

Modernization and Migration

• Develop a phased roadmap for transitioning Loans and financial data workloads to the Lakehouse.
• Identify migration sequencing, dependencies, risks, downstream impacts, and coexistence requirements.
• Define reconciliation, validation, and history-preservation controls.
• Support migration design, testing, reconciliation, production readiness, and implementation reviews.
• Ensure continuity of critical Loans reporting and analytical services during the transition.

Governance, Security, and Data Quality

• Incorporate data classification, privacy, security, retention, and access-control requirements into solution designs.
• Define architecture standards for metadata, lineage, cataloging, data discovery, and controlled data sharing.
• Establish data quality rules, thresholds, monitoring, reconciliation, and exception-management patterns.
• Ensure traceability from source systems through transformations to data products and downstream reports.
• Participate in applicable data governance and architecture review processes.

Technical Leadership

• Develop architecture standards, reference patterns, decision records, templates, and implementation guidance.
• Conduct architecture and design reviews for data platforms, pipelines, data products, migrations, and reporting solutions.
• Provide technical directions to data engineers, developers, analysts, reporting teams, and delivery partners.
• Communicate architecture decisions, risks, dependencies, constraints, and recommendations to project management and stakeholders.
• Provide knowledge transfer and technical guidance to internal teams.

Required Qualifications and Experience
The proposed resource must have:

• A master s degree with at least 10 years of relevant experience, or a bachelor s degree with at least 12 years of relevant experience.
• Demonstrated experience designing and delivering enterprise-scale data architecture solutions.
• Significant experience with Azure Databricks and Azure-based data Lakehouse architecture.
• Experience with Azure Data Lake Storage and relevant Azure data integration, warehousing, security, and monitoring services.
• Strong knowledge of Delta Lake, scalable data processing, metadata, lineage, cataloging, and access control.
• Proven experience developing conceptual, logical, physical, dimensional, and Lakehouse data models.
• Experience designing operational data stores, data warehouses, data marts, data lakes, and analytical platforms.
• Experience defining and delivering governed data products.
• Experience with enterprise data governance, metadata management, data lineage, data quality, and controlled data sharing.
• Demonstrated experience designing and implementing complex data migrations and legacy platform modernization.
• Experience supporting complex transactional or financial data environments. Experience with Loans, banking, treasury, risk, or accounting data is preferred.
• Strong analytical, problem-solving, documentation, communication, and stakeholder-management skills.
• Ability to communicate complex technical concepts to both business and technical audiences.

Relevant Microsoft Azure, Databricks, enterprise architecture, or data management certifications are desirable.

EEO:

Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.