Principal Data Center & Cloud Engineering Lead and Strategist

Overview

TISTA | Department of Veterans Affairs | Infrastructure Engineering & Operations (IE&O)

Employment:

Full-time  |  Proposed reporting: IE&O Program Manager

TISTA is seeking a P rincipal-level Data Center and Cloud Engineering Lead and Strategist to shape and deliver the long-term infrastructure direction for VA. This role combines enterprise strategy, physical data center engineering, hybrid-cloud architecture, AI infrastructure planning, and hands-on automation leadership.

The successful candidate can assess conditions at the rack level, identify the constraints behind an engineering decision, and turn those findings into a defensible ten-year strategy and practical modernization plan. Working across engineering, operations, financial, and provider teams, this leader develops the designs, reusable patterns, and validation evidence needed to implement and sustain approved changes.

This is a hands-on technical leadership role—not an advisory-only architect, a cloud-only specialist, or a project manager without physical infrastructure depth.

At TISTA, you’ll do meaningful, mission‑driven work that improves lives alongside teammates you trust and leaders who are transparent and supportive. We invest in your learning and internal mobility so you can build a career that keeps advancing. We’re proud to serve and hire Veterans, and we put people first in everything we do.

TISTA associates enjoy above Industry Healthcare Benefits, Remote Working Options, Paid Time Off, Training/Certification opportunities, Healthcare Savings Account & Flexible Savings Account, Paid Life Insurance, Short-term & Long-term Disability, 401K Match, Tuition Reimbursement, Employee Assistance Program, Paid Holidays, Military Leave, and much more!

Responsibilities

• Ten-year data center and hybrid-cloud strategy: Lead development and refresh of a ten-year estate strategy covering site roles, capacity, resilience, AI demand, lifecycle exposure, consolidation, cloud placement, and retirement. Compare options using documented assumptions, cost and risk scenarios, and investment dependencies. Translate long-term direction into a rolling three-year roadmap, annual priorities, and executable engineering work packages; update the strategy as evidence and mission needs change.
• Hands-on site and rack-level assessments: Personally conduct onsite walkthroughs and reconcile rack elevations, equipment, power connections, cabling, labels, network paths, environmental readings, and asset records. Evaluate rack density, A/B power paths, power distribution units, airflow, hot/cold aisle containment, cooling constraints, physical clearances, and maintenance access. Distinguish installed capacity from usable capacity under normal, failure, and maintenance conditions, coordinating electrical and mechanical validation with qualified facilities specialists.
• Hybrid architecture and workload placement: Develop integrated compute, storage, network, virtualization, container, backup, and recovery designs across data centers and cloud. Determine whether workloads should remain, consolidate, refresh, migrate, modernize, or retire. Assess application and mainframe dependencies with their accountable owners. Produce reference architectures, placement recommendations, migration waves, and supportable designs without assuming that cloud migration is always the preferred answer.
• AI-ready infrastructure engineering: Assess how AI training, fine-tuning, and inference requirements affect accelerator/GPU capacity, memory, rack density, power, cooling, networking, storage throughput, data locality, and resiliency. Evaluate air- and liquid-cooling options where relevant, facility readiness, utilization, and cloud-versus-on-premises economics. Convert AI demand into right-sized engineering requirements and phased options rather than unsupported hardware forecasts.
• Data center automation and Golden Paths: Lead an automation roadmap covering approved bare-metal, virtual, and cloud provisioning; configuration and firmware lifecycle; patching; network/storage integration; tagging; inventory reconciliation; backup onboarding; and decommissioning. Develop and review reusable infrastructure-as-code modules, scripts, and orchestration workflows with Git-based version control, automated testing, policy checks, controlled credentials, and rollback. Connect deployments to ITSM, monitoring, and configuration records so the delivered service remains traceable and supportable.
• AI-assisted engineering and operations: Partner with data, AI, and operations specialists to evaluate approved use cases such as anomaly detection, capacity forecasting, predictive maintenance, incident correlation, and assisted diagnostics. Define data-quality prerequisites, baselines, evaluation criteria, human oversight, and measurable benefits. Distinguish validated recommendations from unverified model output; maintain approval, security, and recovery controls before any production action.
• Engineering assurance, testing, and operational readiness: Own the technical quality of assigned assessments, designs, and implementation packages. Direct and critically review airflow/thermal studies, including computational fluid dynamics where required, and reconcile their assumptions to actual site conditions. Establish integration, capacity, performance, failover, recovery, and acceptance tests. Verify cutover and rollback plans, maintenance windows, operating procedures, support ownership, and readiness evidence before recommending release.
• Engineering records, lifecycle, and economic evidence: Maintain accurate rack layouts, drawings, cable and power schedules, dependency maps, lifecycle records, and DCIM/CMDB relationships. Use the READI approach—Reliability, Evidence, Asset Lifecycle, Design Risk, and Infrastructure Capacity—to connect findings to decisions. Work with FinOps and the Business Office on total cost, cost of inaction, utilization, capacity release, and benefit validation. Do not equate fewer racks or deployed automation with proven savings.
• Technical leadership and engineering excellence: Serve as a senior technical leader within IE&O Engineering, Architecture, Strategy and Governance (EAS). Lead multidisciplinary engineers; mentor staff; facilitate design reviews; and build reusable patterns through the Automation and Engineering Center of Excellence. Coordinate with Application Hosting, Application Services, cloud, DCO, SDI, security, and facilities providers. Brief VA leaders on alternatives, costs, risks, dependencies, and decisions while maintaining hands-on credibility with delivery teams.
• Representative Outputs: Ten-year estate strategy and phased investment options; rolling modernization roadmap; site/rack assessment packages; AI-readiness assessments; reference architectures; tested Golden Path automation; migration and rollback plans; technical validation reports; and current configuration and support documentation.

Role Boundaries:

The role leads assigned contractor engineering and technical assurance. VA retains applicable funding, policy, risk-acceptance, authorization, contractual, and acceptance decisions. Accountable providers retain their implementation and operational responsibilities. Electrical/mechanical work remains with appropriately qualified personnel; AI use and automation require approved tools, data access, scope, and controls.

Qualifications

Required Qualifications:

Experience:

15+ years of progressively responsible infrastructure engineering experience, including 8+ years in data center engineering/architecture and 5+ years leading complex engineering or modernization initiatives. These periods may overlap. Demonstrated responsibility across physical infrastructure and hybrid-cloud environments.

Physical engineering depth:

Recent, personally performed data center assessments and practical command of rack elevations, power-path redundancy, PDUs, airflow/cooling, cabling, connectivity, equipment placement, capacity, and as-built validation. Ability to read and challenge electrical single-line, mechanical, network, and rack documentation with the appropriate specialists.

Strategy and delivered outcomes:

Evidence of authoring a multi-year infrastructure or data center strategy and translating it into implemented changes. Direct technical leadership of consolidation, migration, refresh, resilience, or decommissioning work, with a clear account of personal contribution and results.

Cloud and platform engineering:

Substantial experience with at least one major cloud platform and enterprise compute, storage, networking, virtualization, and backup/recovery. Ability to assess workload placement, hybrid connectivity, resilience, capacity, and lifecycle tradeoffs.

Automation and AI readiness:

Hands-on ability to develop or review infrastructure automation, APIs, source-controlled configurations, tests, and recovery logic. Experience with Ansible, Terraform, Python, PowerShell, or comparable tools; expertise in every listed tool is not required. Demonstrated ability to reason through AI infrastructure requirements and safely evaluate AI-enabled operational improvements.

Site engagement and Government access:

Ability to conduct required onsite assessments and travel to designated facilities under applicable safety and access procedures. Ability to satisfy the position's Government background-investigation, suitability, training, and system-access requirements.

Preferred Qualifications

• Experience supporting VA, another federal agency, or a large regulated enterprise; AI/HPC or GPU-platform planning and deployment; high-density or liquid-cooled infrastructure; thermal/CFD analysis or digital-twin validation; ITSM/DCIM integration; and building an engineering or automation Center of Excellence.
• Relevant data center design, cloud architecture, automation, or infrastructure credentials are beneficial. Certifications supplement—not replace—demonstrated site engineering and delivery experience. This position is not intended as an AI research-scientist role.
• Internal posting note: Experience thresholds and reporting alignment are proposed recruiting criteria, not verified labor-category minimums. HR and Contracts must confirm the labor category, equivalencies, work location, travel, and suitability/access terms before posting.
• Education and communication: Bachelor's degree in engineering, computer science, information technology, or a related discipline, or an allowable equivalent combination of education and experience. Strong technical writing and the ability to defend designs with engineers and explain investment choices to executive leaders.
• Clearance: Must be able to obtain and maintain the personnel suitability, credentials, system access, and training required per a Tier 4 / High Risk clearance.
• Work Location: Onsite data center assessments and travel are required; base location and frequency to be confirmed.

Responsibilities

• Ten-year data center and hybrid-cloud strategy: Lead development and refresh of a ten-year estate strategy covering site roles, capacity, resilience, AI demand, lifecycle exposure, consolidation, cloud placement, and retirement. Compare options using documented assumptions, cost and risk scenarios, and investment dependencies. Translate long-term direction into a rolling three-year roadmap, annual priorities, and executable engineering work packages; update the strategy as evidence and mission needs change.
• Hands-on site and rack-level assessments: Personally conduct onsite walkthroughs and reconcile rack elevations, equipment, power connections, cabling, labels, network paths, environmental readings, and asset records. Evaluate rack density, A/B power paths, power distribution units, airflow, hot/cold aisle containment, cooling constraints, physical clearances, and maintenance access. Distinguish installed capacity from usable capacity under normal, failure, and maintenance conditions, coordinating electrical and mechanical validation with qualified facilities specialists.
• Hybrid architecture and workload placement: Develop integrated compute, storage, network, virtualization, container, backup, and recovery designs across data centers and cloud. Determine whether workloads should remain, consolidate, refresh, migrate, modernize, or retire. Assess application and mainframe dependencies with their accountable owners. Produce reference architectures, placement recommendations, migration waves, and supportable designs without assuming that cloud migration is always the preferred answer.
• AI-ready infrastructure engineering: Assess how AI training, fine-tuning, and inference requirements affect accelerator/GPU capacity, memory, rack density, power, cooling, networking, storage throughput, data locality, and resiliency. Evaluate air- and liquid-cooling options where relevant, facility readiness, utilization, and cloud-versus-on-premises economics. Convert AI demand into right-sized engineering requirements and phased options rather than unsupported hardware forecasts.
• Data center automation and Golden Paths: Lead an automation roadmap covering approved bare-metal, virtual, and cloud provisioning; configuration and firmware lifecycle; patching; network/storage integration; tagging; inventory reconciliation; backup onboarding; and decommissioning. Develop and review reusable infrastructure-as-code modules, scripts, and orchestration workflows with Git-based version control, automated testing, policy checks, controlled credentials, and rollback. Connect deployments to ITSM, monitoring, and configuration records so the delivered service remains traceable and supportable.
• AI-assisted engineering and operations: Partner with data, AI, and operations specialists to evaluate approved use cases such as anomaly detection, capacity forecasting, predictive maintenance, incident correlation, and assisted diagnostics. Define data-quality prerequisites, baselines, evaluation criteria, human oversight, and measurable benefits. Distinguish validated recommendations from unverified model output; maintain approval, security, and recovery controls before any production action.
• Engineering assurance, testing, and operational readiness: Own the technical quality of assigned assessments, designs, and implementation packages. Direct and critically review airflow/thermal studies, including computational fluid dynamics where required, and reconcile their assumptions to actual site conditions. Establish integration, capacity, performance, failover, recovery, and acceptance tests. Verify cutover and rollback plans, maintenance windows, operating procedures, support ownership, and readiness evidence before recommending release.
• Engineering records, lifecycle, and economic evidence: Maintain accurate rack layouts, drawings, cable and power schedules, dependency maps, lifecycle records, and DCIM/CMDB relationships. Use the READI approach—Reliability, Evidence, Asset Lifecycle, Design Risk, and Infrastructure Capacity—to connect findings to decisions. Work with FinOps and the Business Office on total cost, cost of inaction, utilization, capacity release, and benefit validation. Do not equate fewer racks or deployed automation with proven savings.
• Technical leadership and engineering excellence: Serve as a senior technical leader within IE&O Engineering, Architecture, Strategy and Governance (EAS). Lead multidisciplinary engineers; mentor staff; facilitate design reviews; and build reusable patterns through the Automation and Engineering Center of Excellence. Coordinate with Application Hosting, Application Services, cloud, DCO, SDI, security, and facilities providers. Brief VA leaders on alternatives, costs, risks, dependencies, and decisions while maintaining hands-on credibility with delivery teams.
• Representative Outputs: Ten-year estate strategy and phased investment options; rolling modernization roadmap; site/rack assessment packages; AI-readiness assessments; reference architectures; tested Golden Path automation; migration and rollback plans; technical validation reports; and current configuration and support documentation.

Role Boundaries:

The role leads assigned contractor engineering and technical assurance. VA retains applicable funding, policy, risk-acceptance, authorization, contractual, and acceptance decisions. Accountable providers retain their implementation and operational responsibilities. Electrical/mechanical work remains with appropriately qualified personnel; AI use and automation require approved tools, data access, scope, and controls.

Qualifications

Required Qualifications:

Experience:

15+ years of progressively responsible infrastructure engineering experience, including 8+ years in data center engineering/architecture and 5+ years leading complex engineering or modernization initiatives. These periods may overlap. Demonstrated responsibility across physical infrastructure and hybrid-cloud environments.

Physical engineering depth:

Recent, personally performed data center assessments and practical command of rack elevations, power-path redundancy, PDUs, airflow/cooling, cabling, connectivity, equipment placement, capacity, and as-built validation. Ability to read and challenge electrical single-line, mechanical, network, and rack documentation with the appropriate specialists.

Strategy and delivered outcomes:

Evidence of authoring a multi-year infrastructure or data center strategy and translating it into implemented changes. Direct technical leadership of consolidation, migration, refresh, resilience, or decommissioning work, with a clear account of personal contribution and results.

Cloud and platform engineering:

Substantial experience with at least one major cloud platform and enterprise compute, storage, networking, virtualization, and backup/recovery. Ability to assess workload placement, hybrid connectivity, resilience, capacity, and lifecycle tradeoffs.

Automation and AI readiness:

Hands-on ability to develop or review infrastructure automation, APIs, source-controlled configurations, tests, and recovery logic. Experience with Ansible, Terraform, Python, PowerShell, or comparable tools; expertise in every listed tool is not required. Demonstrated ability to reason through AI infrastructure requirements and safely evaluate AI-enabled operational improvements.

Site engagement and Government access:

Ability to conduct required onsite assessments and travel to designated facilities under applicable safety and access procedures. Ability to satisfy the position's Government background-investigation, suitability, training, and system-access requirements.

Preferred Qualifications

• Experience supporting VA, another federal agency, or a large regulated enterprise; AI/HPC or GPU-platform planning and deployment; high-density or liquid-cooled infrastructure; thermal/CFD analysis or digital-twin validation; ITSM/DCIM integration; and building an engineering or automation Center of Excellence.
• Relevant data center design, cloud architecture, automation, or infrastructure credentials are beneficial. Certifications supplement—not replace—demonstrated site engineering and delivery experience. This position is not intended as an AI research-scientist role.
• Internal posting note: Experience thresholds and reporting alignment are proposed recruiting criteria, not verified labor-category minimums. HR and Contracts must confirm the labor category, equivalencies, work location, travel, and suitability/access terms before posting.
• Education and communication: Bachelor's degree in engineering, computer science, information technology, or a related discipline, or an allowable equivalent combination of education and experience. Strong technical writing and the ability to defend designs with engineers and explain investment choices to executive leaders.
• Clearance: Must be able to obtain and maintain the personnel suitability, credentials, system access, and training required per a Tier 4 / High Risk clearance.
• Work Location: Onsite data center assessments and travel are required; base location and frequency to be confirmed.