Senior Technical Consultant, Modern Data Center

About the role

This role provides senior technical leadership for client engagements in modern data center initiatives. Responsibilities span discovery, design, implementation, and troubleshooting for compute and virtualization solutions.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

Requirements

The posting states a bachelor's degree requirement. Five or more years of experience designing, implementing, or supporting enterprise compute and virtualization environments.

Demonstrated hands-on experience with Microsoft Hyper-V and Windows Server.

Experience designing, deploying, and troubleshooting Hyper-V failover clusters, Cluster Shared Volumes, live migration, virtual networking, and high-availability workloads.

Hands-on experience with Azure Local or Azure Stack HCI, including Azure Arc registration, Storage Spaces Direct, Network ATC, cluster validation, and lifecycle operations.

Experience with Windows Server administration, Active Directory, DNS, Group Policy, Windows Failover Clustering, and PowerShell.

Experience with server hardware, firmware, host configuration, capacity planning, performance analysis, and operational support.

Ability to troubleshoot complex infrastructure issues methodically and drive them to resolution.

Experience creating technical documentation and communicating implementation details to clients and delivery teams.

Demonstrated ability to deliver high levels of customer satisfaction through clear communication, relationship building, expectation management, and professional follow-through.

Ability to independently lead technical workstreams while collaborating effectively with project managers, architects, engineers, vendors, and client stakeholders.

Willingness to travel to client sites as required by engagements.

Practical notes

This role operates in a United States-based engagement model.

Benefits may include medical, dental, vision insurance, 401(k), paid holidays, paid time off, paid parental and caregiver leave, and additional details available on the benefits page.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Modern data center work combines cloud and on-premises infrastructure with automation and analytics.

The role relies on Microsoft Azure, Hyper-V, Windows Server, and related Microsoft ecosystem technologies.

Consulting skills are essential for translating client requirements into technical solutions.

Success depends on structured delivery methods, continuous learning, and collaboration across cross-functional teams.

The position supports enterprise digital transformation through virtualization, hybrid cloud, and infrastructure automation.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.