About the Position

The Data Architect will be responsible for defining and evolving the customer’s enterprise data architecture and establishing the architectural principles, standards, patterns, and roadmaps required to support secure, scalable, governed, and modern data capabilities.

This role will provide architectural leadership across enterprise data platforms, data integration, data modeling, analytics, artificial intelligence, data governance, and cloud technologies. The Data Architect will partner with business stakeholders, Data Engineers, Data Scientists, application teams, cybersecurity, infrastructure, and other technology teams to ensure that data solutions align with customer business strategy, enterprise architecture, security requirements, and long-term technology objectives.

The Data Architect will establish the architectural direction and guardrails within which engineering, analytics, and AI solutions are implemented, while providing design oversight for strategic and complex data initiatives.

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What you bring

• Demonstrated experience designing architecture for enterprise-scale data platforms and complex data ecosystems.
• Deep understanding of modern architectures including:
  
  • Data lakehouse
  • Data warehouse
  • Data lake
  • Data Mesh
  • Data Fabric
  • Data products
  • Semantic layers
  • Metadata and lineage
  • Master and reference data
• Strong knowledge of conceptual, logical, physical, dimensional, and analytical data modeling.
• Strong knowledge of data integration architectural patterns including ETL/ELT, APIs, event-driven integration, CDC, data sharing, and federation.
• Experience designing cloud-based data architectures using Microsoft Azure or comparable cloud technologies.
• Strong architectural understanding of technologies such as Databricks, Delta Lake, Unity Catalog, Azure SQL, SQL Server, Power BI, and related Azure data services.
• Understanding of data governance technologies and concepts including metadata management, lineage, data catalogs, quality, security, access control, and lifecycle management.
• Understanding of architectural requirements for machine learning, Generative AI, RAG, vector search, semantic search, and AI agent solutions.
• Working knowledge of SQL, Python, and distributed data-processing technologies such as Apache Spark/PySpark, sufficient to evaluate designs and effectively collaborate with engineering teams.
• Experience developing architecture artifacts including reference architectures, solution architectures, conceptual and logical data models, architecture decision records, technology standards, and roadmaps.
• Strong knowledge of security architecture principles, including identity and access management, least privilege, encryption, network security, secrets management, auditing, and secure data sharing.
• Ability to evaluate technology alternatives and articulate recommendations based on business value, technical fit, cost, security, scalability, maintainability, and risk.
• Ability to communicate complex technical concepts clearly to executives, business stakeholders, architects, engineers, and other audiences.
• Strong leadership, facilitation, negotiation, and stakeholder-management skills.
• Ability to manage multiple strategic initiatives simultaneously and exercise independent judgment in complex and sometimes ambiguous situations

Nice to have

• Experience in financial regulation, audit or federal environments.

Working with us

• You will work inside a secure, private cloud environment with highly confidential data.
• You must be authorized to work in the United States and able to pass a background check.
• We are looking for people who can start within a few weeks.

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What You'll Do

• Define, maintain, and evolve the enterprise data architecture framework, including principles, standards, reference architectures, patterns, and technology guidelines.
• Develop current-state, transition-state, and target-state architectures supporting the customer’s long-term data and technology strategy.
• Develop strategic data architecture roadmaps that align business capabilities, data platforms, technology investments, and modernization initiatives.
• Identify architectural opportunities to simplify the enterprise data landscape, reduce technical debt, limit unnecessary data duplication, and reduce organizational data silos.
• Define reference architectures and architectural patterns for cloud-based enterprise data platforms, including technologies such as Microsoft Azure and Databricks.
• Establish architectural standards for Databricks Lakehouse, Delta Lake, Unity Catalog, Azure SQL, SQL Server, object storage, analytical platforms, and related enterprise data services.
• Define architectural patterns for data ingestion, integration, transformation, storage, sharing, consumption, and archival.
• Establish architectural approaches for batch, near-real-time, event-driven, API-based, and file-based data integration.
• Define architectural principles for availability, scalability, resiliency, recoverability, performance, observability, and cost optimization.
• Establish architecture patterns for development, test, QA, and production data environments, including appropriate separation of workloads and responsibilities.
• Define modeling approaches appropriate for transactional systems, analytical workloads, data warehouses, lakehouses, reporting platforms, and AI/ML workloads.
• Develop and maintain enterprise-level information models representing important business entities, relationships, and shared data concepts.
• Provide architectural guidance for dimensional modeling, semantic models, canonical data models, domain-oriented data products, and other enterprise modeling approaches.
• Establish architectural standards for reference data, master data, metadata, business terminology, data classification, and data lineage.
• Promote reuse of common enterprise data definitions and models to improve consistency and interoperability across business domains.
• Partner with cybersecurity, privacy, governance, legal, records management, and business teams to incorporate governance and compliance requirements into data architectures.
• Establish architectural guidance for role-based and attribute-based data access and least-privilege security models.
• Define architectural standards for securing sensitive and regulated information across data ingestion, storage, processing, sharing, analytics, and AI workloads.
• Define architectural approaches for tools such as Databricks Unity Catalog and Microsoft Purview, where applicable, to support centralized governance, lineage, discovery, and access management.
• Define enterprise integration patterns for moving and sharing data between business applications, SaaS platforms, databases, APIs, files, cloud platforms, and analytical environments.
• Provide architectural direction regarding when data should be copied, federated, virtualized, synchronized, or accessed directly.
• Work with Data Engineers and application teams to ensure implementation approaches conform to approved integration architectures.