Senior Data Architect

Agile Resources, Inc.; Atlanta (GA); Hybrid; USD 198,000 - 242,000; Full time

Benefits offered by this job

Short- and Long-Term Disability

HSA & FSA options

Paid Time Off

Job summary

Agile Resources, Inc. is seeking a Data Architect to lead the modernization of a legacy on-premises data environment into a scalable cloud data platform.

The role emphasizes defining migration approaches, architecture guardrails, and governance to ensure secure, efficient data sharing across domains. The ideal candidate will have hands-on experience transforming enterprise data platforms (Azure, AWS, or GCP) with strong data modeling and security focus, guiding engineering teams to scalable

Qualifications

• Experience leading legacy/on-premises modernization to a cloud data platform.
• Ability to design enterprise-domain data models and cross-system integration patterns.
• Hands-on architecture leadership while guiding engineering teams to production readiness.

Responsibilities

• Lead end-to-end modernization of legacy data environments into a modern cloud data platform.
• Define target architecture for cloud data platforms including data movement and lakehouse patterns.
• Design enterprise and domain data models with standardized definitions for reuse.
• Establish integration and data-sharing patterns across multiple platforms and domains.
• Build and operationalize enterprise data governance, metadata, lineage, cataloging, and data quality controls.
• Drive security and access control architecture with least-privilege design and auditing.

Skills

Legacy modernization

Cloud data platforms

Data modeling

Data governance

Security & access controls

Architectural leadership

Stakeholder collaboration

Tools

Databricks

Delta Lake

Snowflake

Azure Synapse

Azure Data Lake

AWS data services

GCP data services

Job description

Location: Hybrid Remote in Atlanta (2 days onsite/week).

Salary: Up to $220,000 (depending on experience) + bonus

• Short- and Long-Term Disability (STD/LTD)
• HSA & FSA options
• Paid Time Off (PTO)

This Data Architect role owns the end-to-end design and evolution of an enterprise data platform, with a major focus on modernizing legacy data architecture into a scalable cloud-based environment. The ideal candidate has personally led the transformation of an on-premises or legacy data environment into a modern Azure data platform; however, comparable enterprise transformations into AWS or GCP are also highly relevant. Success is defined by establishing a clear target-state architecture, creating consistent data models and integration patterns across systems and domains, and putting governance in place so teams can securely trust, share, and consume enterprise data. The role is hands-on and requires staying close to implementation while setting architectural direction.

Responsibilities:

• Lead the end-to-end modernization of a legacy/on-premises enterprise data environment into a modern cloud data platform, defining migration approach, key milestones, and architectural guardrails.
• Define the enterprise cloud data-platform target architecture, including core platform services, data movement patterns, lakehouse/warehouse approaches, and workload separation aligned to security and performance needs.
• Design enterprise and domain data models, including shared entities, relationships, canonical structures, and standardized definitions that support consistent reuse across systems and domains.
• Establish integration and data-sharing patterns across multiple platforms, including ingestion, CDC/event patterns where appropriate, batch/stream processing considerations, and downstream consumption approaches.
• Build and operationalize enterprise data governance and production architecture, including metadata management, lineage, cataloging, data quality controls, and enforcement of data standards.
• Drive security and access control architecture for data platforms, including role-based access, data classification, least-privilege design, and auditing/monitoring requirements.
• Guide engineering teams through hands-on architecture decisions, reviewing designs and implementation plans to ensure reliability, scalability, and maintainability in production.
• Develop reference architectures, templates, and design standards that accelerate delivery and reduce fragmentation across domains and delivery teams.
• Partner with stakeholders to translate business needs into architectural requirements and prioritize foundational capabilities that unblock downstream analytics and operational use cases.

Required Skills:

• Experience architecting or leading a significant legacy/on-premises enterprise data modernization into a modern cloud data platform (AWS, Azure, or GCP).
• Experience designing enterprise/domain data models and integration patterns, including relationships, shared entities, canonical structures, and data-sharing approaches across multiple systems/domains.
• Strong familiarity defining modern enterprise cloud data-platform target architectures using technologies such as Databricks, Delta Lake, Snowflake, Azure Data Lake/Synapse/Fabric, AWS data services, GCP data services, or equivalent cloud-native platforms.
• Hands-on experience establishing enterprise data governance and production architecture, including metadata, lineage, data quality, cataloging, security/access controls, scalability, reliability, and data standards.
• Demonstrated ability to provide senior, hands-on architecture leadership by setting architectural direction and making enterprise-level design decisions while staying close to implementation to guide engineering teams and delivery.

Preferred Skills:

• Direct experience modernizing an on-premises or legacy data architecture into a modern cloud ecosystem, particularly using Azure (Databricks, ADLS, Delta Lake, Synapse, Microsoft Fabric), AWS (such as S3, Redshift, Glue, EMR), or GCP (such as BigQuery, Dataflow, Dataproc).
• Production Lakehouse experience with Databricks and Delta Lake, including Medallion/Bronze-Silver-Gold architecture patterns.
• Experience building enterprise semantic models, standardized KPIs, and reusable data definitions across multiple business domains or product modules.
• Experience designing data foundations that support AI/ML, RAG, or GenAI use cases.