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Added 1 min ago
Application Engineer
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Manage workstreams within AI, data, cloud, and digital engineering programs from assessment and design through implementation and adoption.

General Datatech, LP

Practice Director, Data Architecture

Remote, United States

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Job details

Posting language: English
Posted: Nov 2, 2026

Original job description

Join GDT and be part of a dynamic team where innovation meets excellence! At GDT, our employees are the cornerstone of our success. GDT offers a people-first culture focused on inclusivity, growth, and innovation. Employees have access to career development opportunities, cutting-edge technology, and a collaborative environment. Employees are empowered to make a meaningful impact and are recognized for their contributions. As a global IT services company, we prioritize attracting, empowering, and retaining top-tier talent dedicated to delivering exceptional results.

Job Summary The Practice Director, Data Architecture partners with GDT clients to design and govern enterprise data frameworks, pipelines, and cloud storage systems optimized to feed and support artificial intelligence (AI), machine learning (ML), and large language model (LLM) workloads. The role translates business objectives into secure, scalable, and cost-effective data architectures that support trusted AI outcomes. This client-facing role works across data science, data engineering, software development, security, infrastructure, and business teams to guide strategy and delivery. This role also helps build GDT’s Data Practice by developing reusable methods, reference architectures, delivery standards, and solution capabilities.

Key Responsibilities

· Lead discovery, assessment, and architecture engagements to define the data foundation for client AI, ML, and LLM initiatives. · Build and oversee scalable ingestion, storage, and processing pipelines using ETL/ELT patterns for large volumes of structured and unstructured data. · Design data flows for training, fine-tuning, retrieval-augmented generation (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 based on client needs. · Recommend modular, portable technology patterns that support interoperability, avoid unnecessary vendor lock-in, and scale with evolving workloads. · Translate business goals and operating 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 for delivery teams. · Support presales, scoping, estimates, statements of work, implementation guidance, operational readiness, and knowledge transfer. · Help build and mature the 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. Knowledge, Skills, and Abilities · 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 one or more major platforms: AWS (including Redshift and SageMaker), Microsoft Azure (including Azure ML), or Google Cloud (including Vertex AI). · 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 that support consistent, repeatable designs. · Mastery in requirements gathering and analysis, with the ability to translate business needs into technical architectures and roadmaps. · Excellent communication, presentation, facilitation, and stakeholder-management skills, including the ability to explain complex technical concepts to diverse audiences. · Self-starter able to operate with a high degree of latitude, handle complex issues, and drive outcomes across multiple stakeholders.

Experience · Minimum 7+ years of experience in data architecture, data engineering, cloud architecture, analytics platforms, or a related discipline, including enterprise-scale solution design. · Demonstrated experience designing and governing data platforms and pipelines that support AI/ML workloads in production environments. · Experience working directly with clients and account teams to understand business outcomes, define requirements, and translate them into secure, scalable data architectures and delivery plans.

Demonstrated background in:

· Enterprise data architecture and modeling, including conceptual, logical, and physical data models. · Cloud data platforms across AWS, Microsoft Azure, and/or Google Cloud Platform. · Scalable ingestion and processing patterns for structured and unstructured data, including ETL/ELT, batch, and streaming architectures. · AI/ML data foundations, including feature stores, vector databases, RAG pipelines, embeddings, model fine-tuning, and inference architectures. · Data governance, lineage, quality, security, privacy, compliance, and observability. · Client-facing consulting, architecture reviews, roadmap planning, and cross-functional delivery leadership.

Education · Bachelor’s degree in Computer Science, Data Science, Information Systems, Information Technology, Engineering, or a related field; equivalent experience will be considered.

Certifications

Preferred (nice to have):

· AWS Certified Solutions Architect, AWS Certified Machine Learning, or comparable AWS certification. · Microsoft Certified: Azure Solutions Architect Expert, Azure AI Engineer Associate, or comparable Azure certification. · Google Professional Cloud Architect, Professional Machine Learning Engineer, or comparable Google Cloud certification. · Relevant Databricks, Snowflake, Kubernetes, data governance, security, or MLOps certifications.

GDT IS AN EQUAL OPPORTUNITY EMPLOYER. Applicants and employees are considered for positions and are evaluated without regard to mental or physical disability, race, color, religion, gender, national origin, age, genetic information, military or veteran status, sex, marital status, gender identity or any other protected Federal, State/Province or Local status unrelated to the performance of the work involved.

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