Hiring a head of data infrastructure is an executive decision about how your company will build, govern, and operate the systems behind trusted data. The right leader connects platform architecture to business priorities, sets standards for reliability and access, and builds the team that can support the next stage of growth. This guide explains how to define the role, assess candidates, run the search, and set useful first-90-day outcomes.
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What is a head of data infrastructure?
A head of data infrastructure is the senior leader responsible for the platforms, systems, operating standards, and team that make data available for analytics, products, machine learning, and decision-making. The role usually spans architecture, data movement, storage, reliability, security, governance, and people leadership. Unlike an individual contributor data infrastructure engineer, this leader sets direction and is accountable for how the function performs over time.
The title is not fully standardized. In one company, the role may be a player-coach building the first reliable data platform. In another, it may lead several teams across data engineering, platform engineering, and machine learning infrastructure. The job description should describe the mandate, decision rights, team size, and business outcomes instead of relying on the title alone.
Decide whether this is the right executive hire
Before opening the search, define the business problem this leader must solve. A head of data infrastructure is usually appropriate when data reliability, platform scale, delivery speed, or cross-functional ownership has become a leadership constraint. The company may have good engineers but no shared architecture, repeated incidents, unclear data ownership, or a growing set of analytics and AI workloads competing for the same foundation.
The hire is less appropriate when the company needs a hands-on engineer to own a bounded project, a data engineering manager to lead a small delivery team, or a chief data officer to represent data strategy, risk, and investment at the board level. Those roles may collaborate closely with the head of data infrastructure, but they are not interchangeable.
Use these questions to pressure-test the need:
- Which business or product decisions are being slowed by unreliable, inaccessible, or poorly governed data?
- Who currently owns platform architecture and the trade-offs between speed, cost, security, and resilience?
- How many teams depend on the data foundation today, and how many will depend on it over the next year?
- Does the new leader have authority to set standards, prioritize platform work, and hire the team required to deliver it?
- What must be measurably better six and twelve months after the hire?
If the answers are unclear, pause the search. Senior candidates will notice when a company wants executive-level accountability without giving the role a defined problem or the authority to solve it.
Define the role's remit and reporting line
A strong brief gives candidates a precise view of what they will own. The remit should cover the data foundation without quietly absorbing every technology problem in the business.
Architecture and platform direction
The head of data infrastructure sets the principles for ingestion, storage, transformation, orchestration, serving, metadata, and observability. They do not need to dictate one tool in the job description. They do need to explain the architectural constraints, the current state, and the decisions the new leader can revisit.
Reliability and operational performance
The leader owns the operating model for dependable data services. That includes clear service expectations, incident response, recovery practices, capacity planning, and a way to distinguish a one-off pipeline failure from a systemic platform problem. Reliability should be discussed in terms of user impact, not just whether a scheduled job completed.
Governance, security, and access
Data infrastructure creates the pathways through which sensitive and business-critical information moves. The leader should partner with security, legal, privacy, and product stakeholders to make access understandable and auditable. The role is not a substitute for a privacy officer or security executive, but it must turn those requirements into workable engineering standards.
The NIST AI Risk Management Framework is a useful reference when the platform supports AI workloads. It reinforces the need to connect technical controls with governance, measurement, and accountability rather than treating risk as a separate document.
Team design and talent development
The leader decides which capabilities belong in the core platform team, which should remain close to product or analytics groups, and where specialist skills are needed. The remit should include hiring, coaching, succession planning, and a practical approach to technical standards. Candidates should understand whether they are inheriting a team, building one, or doing both.
Where should a head of data infrastructure sit?
The reporting line should reflect the decisions the role must make. Reporting to a CTO is often effective when the main mandate is platform architecture, engineering quality, and delivery across product teams. Reporting to a chief data officer can work when the organization already has an established data strategy and needs a strong technical counterpart. A direct CEO relationship may be appropriate during a major data transformation, but it should come with clear partnership expectations with engineering and security.
Do not use a reporting line as a substitute for operating clarity. Document the role's decision rights in the brief:
- Which platform standards can the leader set without additional approval?
- Which budget and headcount decisions belong to the role?
- How are platform priorities balanced against product delivery?
- Who resolves conflicts between central standards and team-level autonomy?
- Which risk, reliability, and delivery outcomes will appear in executive reviews?
A candidate may accept a demanding transformation. They are less likely to accept responsibility for outcomes that remain controlled by several people outside the role.
Assess architecture and governance leadership
The best candidates combine technical judgment with executive operating discipline. They can explain architecture choices in business terms, identify where standards are necessary, and avoid imposing complexity before the organization needs it.
Architecture depth
Look for evidence that the candidate has made trade-offs across batch and streaming workloads, data modeling, storage, orchestration, platform interfaces, and access patterns. The interview should test how they make decisions under constraints. Ask what they would centralize, what they would leave to product teams, and how they would decide when a platform abstraction has earned its place.
A useful design discussion starts with a business scenario rather than a preferred tool. For example, ask the candidate to design a foundation for operational reporting, experimentation, and machine learning while the organization is growing quickly. Probe the assumptions behind their design, the failure modes they would plan for, and the order in which they would deliver the work.
Governance that teams can use
Governance is stronger when engineers and data users can follow it without creating a shadow process. Ask candidates how they would define ownership, classify sensitive data, document lineage, manage access, and handle exceptions. Strong answers show how policy becomes a repeatable workflow with useful feedback, not a collection of rules that slow every request.
For observability, the OpenTelemetry documentation offers a helpful example of a shared approach to collecting and exporting telemetry. The hiring question is not whether a candidate can recite a standard. It is whether they can create consistent signals, define service expectations, and help teams act on them.
Executive communication
Ask for an example of explaining an infrastructure trade-off to a non-technical executive. The candidate should be able to state the decision, the options considered, the risk of delay, the expected outcome, and what will be measured. A leader who can only communicate through technical detail will struggle to protect platform investment when priorities change.
Build a scorecard for the search
Scorecards make executive hiring more consistent when they describe observable evidence instead of broad personality traits. Weight the criteria according to the company's stage and mandate.
| Scorecard area | What strong evidence looks like |
|---|---|
| Technical architecture | Has made clear trade-offs across data platforms, interfaces, reliability, and future change. |
| Operating leadership | Has created planning, incident, prioritization, and delivery systems that teams actually use. |
| Governance and risk | Can translate security, privacy, access, and quality needs into practical engineering controls. |
| Organizational design | Can define team boundaries, hiring priorities, ownership, and collaboration with product and security. |
| Business judgment | Connects infrastructure investment to customer experience, decision quality, revenue, or risk reduction. |
| Executive communication | Can build alignment, make trade-offs visible, and give leaders a concise view of progress and risk. |
Use a defined scale and require interviewers to record evidence for each score. Avoid adding criteria late in the process because a favored candidate is weak in an agreed area. If the role is a player-coach position, give hands-on architecture and technical judgment more weight. If the role leads a larger organization, increase the weight on operating systems, team design, and executive influence.
Source candidates beyond familiar networks
Senior data infrastructure leaders are often found through adjacent titles and carefully mapped organizations, not only through exact-title searches. Build a market map around the problems the candidate has solved: platform scale, data reliability, governance, AI infrastructure, developer productivity, or the creation of a data organization.
Prioritize outreach that gives a credible reason for the conversation. Explain the company's stage, the platform challenge, the scope of authority, and the outcome the hire can shape. Experienced leaders respond to a clear mandate more than a list of tools.
Screen for the context behind each achievement. A candidate who improved reliability on a large established platform may be excellent, but the role may require someone who can build systems with limited support. Conversely, a startup builder may need to show that they can introduce operating discipline across a larger, more complex organization. The question is not whether the candidate has seen the same stack. It is whether they can recognize the current constraint and lead the organization through it.
People In AI's data infrastructure and MLOps expertise is a relevant internal resource for teams mapping this market. For an individual contributor search, the ML infrastructure engineer hiring guide addresses a different level of the organization and should not be used as a substitute for this executive brief.
Run a structured executive interview loop
A focused interview loop should test the scorecard without asking every interviewer to repeat the same technical screen. Share the mandate and evaluation criteria before interviews begin.
- Executive alignment call: The CEO, CTO, or hiring executive explains the business context, decision rights, and outcomes. The candidate can test whether the mandate is real.
- Career and scale interview: Explore the candidate's progression, team sizes, operating context, and the problems they personally owned. Ask what changed because of their leadership.
- Architecture working session: Present a realistic scenario and evaluate assumptions, trade-offs, sequencing, and failure planning. Do not grade candidates on whether they select the same tools as the panel.
- Governance and risk interview: Discuss access, quality, lineage, privacy, security partnership, incident learning, and how the candidate handles exceptions.
- Org design and talent interview: Ask how they would structure the team, hire the first critical roles, develop managers, and work with product, analytics, and machine learning teams.
- Stakeholder references: Speak with former peers, direct reports, and executive partners. Test the same scorecard areas with specific questions rather than asking only whether the person was well liked.
Keep the loop long enough to create confidence but short enough to respect senior candidates. Give candidates the information they need to prepare for a working session, and tell them how decisions will be made. A vague or constantly changing process is a signal about the company the candidate is considering joining.
Get specialist support for your head of data infrastructure search
Set practical first-90-day expectations
The first 90 days should produce understanding, alignment, and a focused delivery plan. Do not make the new leader promise a total platform redesign before they have examined the current system and listened to its users.
Days 1 to 30: learn and baseline
The leader should meet engineering, product, analytics, machine learning, security, and executive stakeholders. They should map the current architecture, key data products, ownership boundaries, major incidents, delivery bottlenecks, and the highest-risk dependencies. A useful early output is a short baseline that distinguishes urgent reliability issues from strategic platform decisions.
Days 31 to 60: decide and align
Next, the leader should propose a target operating model and a prioritized roadmap. That roadmap should show what to protect, what to simplify, what to retire, and what to build. It should also state the staffing plan, decision owners, dependencies, and measures of progress. The goal is not to produce a perfect future-state diagram. It is to create a sequence the organization can fund and execute.
Days 61 to 90: start delivery
By the end of the first quarter, the leader should have started one or two high-value improvements and established the cadence for platform planning, reliability reviews, and stakeholder communication. Early work might address a clear source of recurring incidents, an access bottleneck, an ownership gap, or a platform capability that several teams need. The first wins should demonstrate judgment and create momentum without locking the company into an untested architecture.
Agree on the first-90-day outcomes before the hire accepts. This creates a fair assessment period and prevents the board, executive team, and new leader from using different definitions of success.
Common hiring mistakes to avoid
Writing a tool list instead of a mandate
A list of platforms can help describe the current environment, but it does not define the leadership challenge. Candidates need to understand the scale, constraints, users, authority, and outcomes behind the stack.
Hiring for the next company instead of the current one
A leader who has run a large organization may not enjoy building a first platform team. A strong builder may not yet have the operating experience required for a complex multi-team environment. Match the search to the stage the company is actually in, not the stage it hopes to reach.
Separating governance from engineering reality
Governance that cannot be implemented will be ignored. Include security, privacy, and data users in the process, then test whether candidates can make standards usable for engineers and stakeholders.
Under-specifying authority
Do not promise ownership of reliability, architecture, and hiring while reserving every meaningful decision for another executive. A credible mandate is part of the offer and a major factor in closing the right candidate.
Relying on one technical interview
Architecture skill matters, but the role also requires prioritization, communication, organization design, and the ability to build trust. Use the full scorecard and gather evidence from multiple perspectives.
Frequently Asked Questions
What does a head of data infrastructure do?
A head of data infrastructure leads the architecture, reliability, governance, and team responsible for the company's data foundation. The role connects platform decisions to business and product needs, sets operating standards, and creates the organization required to support analytics, machine learning, and data products.
What is the difference between a head of data infrastructure and a data engineering manager?
A data engineering manager usually leads a team and delivers a defined area of work. A head of data infrastructure owns a broader technical and organizational mandate, including platform direction, cross-team standards, governance partnership, and long-term team design. The right level depends on the scope of decisions and outcomes, not the title alone.
Should a head of data infrastructure report to the CTO?
A CTO reporting line often works when the role's main responsibilities are platform architecture, engineering quality, reliability, and delivery across product teams. Other structures can work when the company already has a strong data strategy or is running a broader transformation. The key is to define decision rights, budget, hiring authority, and executive partnerships.
How do you interview a head of data infrastructure?
Use a structured loop covering career scale, architecture, reliability, governance, organization design, executive communication, and references. Give the candidate a realistic working scenario and evaluate assumptions, trade-offs, sequencing, and risk. Interviewers should score evidence against the same agreed criteria instead of relying on general impressions.
What should a head of data infrastructure accomplish in the first 90 days?
The first 90 days should produce a current-state baseline, stakeholder alignment, a prioritized roadmap, a staffing plan, and the start of one or two high-value improvements. The leader should understand the system before committing to a redesign, then establish a repeatable cadence for platform planning, reliability, governance, and executive communication.
Where can I find candidates for a head of data infrastructure?
Search across adjacent leadership titles and organizations that have solved comparable platform, reliability, governance, or AI infrastructure problems. Assess the context behind each candidate's work, including company stage, team size, scope of authority, and hands-on depth. Specialist recruiting support can expand the market map and create a more focused executive process.
Talk with People in AI about hiring your next data infrastructure leader
The strongest head of data infrastructure searches start with a clear business problem, a credible mandate, and a scorecard tied to observable evidence. Define the architecture and governance outcomes, give the leader real authority, test the ability to build an organization, and agree on the first 90 days before the search begins. That preparation gives both the company and the candidate a better basis for a durable executive hire.
