Practice Director, Data Architecture

GDT - General Datatech

. Lead discovery, assessment, and architecture engagements for client AI, ML, and LLM initiatives .

Posted 11/2/2026full-timeRemote • North Carolina • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing scalable data architectures and pipelines for AI and ML initiatives across cloud environments. Proficient in translating business requirements into technical solutions while ensuring data governance, security, and compliance.

Highest-signal resume keywords

Cloud Data ArchitectureData Lakes/LakehousesMLOps Tools and PracticesAdvanced SQL and Database DesignClient-Facing Consulting

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingDatabase SecurityPythonApache SparkKafkaRAG PipelinesCI/CDModel Lifecycle ManagementData GovernanceEnterprise-Scale Solution Design

Soft Skills

Excellent CommunicationStakeholder ManagementFacilitationIndependent OperationProblem-Solving

Tools & Technologies

AWSAzureGoogle Cloud PlatformMLflowKubeflowVector DatabasesFeature StoresCloud Storage Patterns

Industry Keywords

Data ArchitectureData EngineeringAnalytics PlatformsAI/ML WorkloadsData QualityData LineagePrivacyCompliance

Tech Stack

Tools & technologies

ApacheAWSAzureCloudETLGoogle Cloud PlatformKafkaPythonSparkSQL

About the role

Key responsibilities & impact

Lead discovery, assessment, and architecture engagements for client AI, ML, and LLM initiatives
Build and oversee scalable ingestion, storage, and processing pipelines for structured and unstructured data
Design data flows for training, fine-tuning, RAG, batch scoring, real-time inference, and downstream applications
Define architecture standards for cloud and hybrid environments across AWS, Azure, and Google Cloud Platform
Design data lakes, lakehouses, warehouses, vector databases, feature stores, and supporting platform services
Recommend modular and portable technology patterns that support interoperability and reduce vendor lock-in
Translate business goals and constraints into architecture decisions, roadmaps, implementation plans, and measurable outcomes
Review and validate solution designs for scalability, operability, portability, security, performance, and cost effectiveness
Create architecture artifacts, reference designs, standards, decision records, and implementation guidance
Support presales, scoping, estimates, statements of work, implementation guidance, operational readiness, and knowledge transfer
Build and mature GDT’s Data Practice through reusable delivery assets, methods, accelerators, and technical standards
Contribute to practice enablement, mentoring, partner alignment, thought leadership, and go-to-market capabilities
Work across data science, data engineering, software development, security, infrastructure, and business teams

Requirements

What you’ll need

Ability to work with clients and account teams to understand functional and non-functional requirements and collaborate across teams to architect, design, validate, and deliver solutions
Strong understanding and practical experience with cloud data and AI services on AWS, Azure, or Google Cloud Platform
Advanced SQL, data modeling, database design, performance, and database security
Python, Apache Spark, Kafka or comparable streaming technologies, and distributed data processing
MLOps tools and practices, including MLflow, Kubeflow, model lifecycle management, CI/CD, and observability
RAG pipelines, embedding generation, vector search, model fine-tuning, evaluation, and inference architectures
Data lakes/lakehouses, data warehouses, vector databases, feature stores, and cloud storage patterns
Data governance, quality, lineage, privacy, security, compliance, and access control
Experience publishing architecture strategies, standards, and guidelines
Mastery in requirements gathering and analysis and translating business needs into technical architectures and roadmaps
Excellent communication, presentation, facilitation, and stakeholder-management skills
Ability to operate independently with a high degree of latitude, handle complex issues, and drive outcomes across multiple stakeholders
Minimum 7+ years of experience in data architecture, data engineering, cloud architecture, analytics platforms, or a related discipline, including enterprise-scale solution design
Experience designing and governing production data platforms and pipelines supporting AI/ML workloads
Experience working directly with clients and account teams to define requirements and translate business outcomes into secure, scalable data architectures and delivery plans
Background in enterprise data architecture and modeling, cloud data platforms, scalable ETL/ELT, batch and streaming architectures, AI/ML data foundations, data governance, and client-facing consulting
Bachelor’s degree in Computer Science, Data Science, Information Systems, Information Technology, Engineering, or a related field; equivalent experience will be considered

Benefits

Comp & perks

Career development opportunities
Access to cutting-edge technology
Collaborative environment
People-first, inclusive culture
Meaningful impact and recognition for contributions