When to hire a data professional
By Francis Addio, Business Analyst; and Lydia Monnington, Data Science Lead and long-term DataKind UK volunteer
It’s time to build the data capacity in your organisation. So where do you start? We’ve launched a short series on the who, what, when, why, and how of hiring a data professional for your organisation.
Why data roles are different
Data professionals sit at the intersection of technology, process, and decision-making. They not only use tools, but also shape how information flows across the organisation. You’re looking for someone who can extract actionable insights from complex data, while communicating clearly to guide business decisions.
These roles are specialised and in high demand; they often work solo in underfunded environments, are managed by non-specialists, and must balance technical skill with communication, governance, domain knowledge, and mission alignment.
Start with your goals
Deciding why you need a data professional should be defined by your business needs, which in turn inform your job description. A problem-first approach ensures you hire the right specialist rather than a generic data person. To help you understand who you’re looking for, imagine two years ahead, when the person you've hired has done an amazing job. What do things look like? What is different because of their work? Try to build as clear an image as you can about these details:
What problem needs solving? eg poor reporting; lack of access to useful insights; collected but unused data.
What outcome is expected? This could be identifying the people your organisation can help most effectively; measuring the impact you have; or increasing the effectiveness of your fundraising.
What kind of outputs and analysis do you need to see? Identify at a high level the work needed to deliver these outputs, for example, creating a map of the UK by level of deprivation compared to current operating areas; collating survey data; or measuring donation rate per email sent.If you’re unsure what outputs and methods you need, a senior data analyst or manager would have the experience to help you think through the key questions and create a data vision or strategy.
Your current state
An effective data team needs to be the right fit for your existing (or planned) roles, systems, and processes. You also need to take an unbiased look at the current state of your data.
How do you collect data?
Is all key data being collected? How accurate is it? Your data collection is hopefully part of some of your team’s usual roles, or is even automated. If you feel that data collection needs improving, look for those skills when hiring.
Is the data clean and ready to use?
You might want to hire someone specifically to clean up and standardise existing data. Data cleaning and preparation is key, but often less visible to the rest of your organisation, so don't underinvest here! If it isn't clean and all in one place, a data engineer will have the skills to tidy it up.
Where does it live?
Is it stored in a database, or in CSV/Excel files? Do you want to move to a new platform, or digitise what you have? Be clear about where your data is stored, as working with a database requires different skills from working with data in files. You may have a platform that is administered by a third-party provider, such as BlackBaud.
If you are lucky enough to have a bespoke or in-house system, you probably have a Database Admin or Data Platform Engineer. A data engineer usually focuses on the data, and a database admin will manage the database itself (creating the database, access, and backups).
Are your systems compliant?
A Data Protection Officer works with everyone in the organisation that handles data to ensure that your systems and processes comply with GDPR and any other relevant legislation (for instance if you are an international organisation). This role doesn’t necessarily fall under the remit of an analyst or data administrator, but may be encompassed by someone who manages your policies, such as an operations manager.
Goals into roles - Understanding what you need
Many managers know they need a ‘data person’ but are unclear on which one to hire, leading to poor matches and rapid turnover. Titles such as 'Data Analyst', 'Business Analyst', or 'Data Engineer' can be used interchangeably, even though the work they do is very different.
Timing also matters. Bringing someone with a specific skill set in too early will be a waste of your resources; setting up crucial systems too late could cause reporting and compliance risks. The table below maps skills to common roles and suggests when to introduce them.
| Title | Role | Examples | When to hire |
|---|---|---|---|
| Business Analyst | Translates needs into solutions. Maps data flows, and improves processes. | Maps referral → service → outcomes data across teams. | Processes are unclear; need requirements and workflows optimised. |
| Data Analyst | Finds and explains insights from data to make key business decisions. Skilled in understanding business problems. Able to communicate data results clearly and make recommendations to the rest of the organisation. | Cleans and analyses data. Data visualisation, may use dashboarding tools or SQL. Builds funder dashboards in Power BI; monthly impact packs for SLT. | Building data processes. Data reporting is slow/ inconsistent. |
| Data Engineer | Automates the cleaning and processing of raw data to make it easier to use. Experienced with data pipelining tools and automation tools. Knowledge of data warehouse design. | Automates data flows from CRM → data warehouse for business intelligence. Usually, this is database-based, using SQL. Builds/maintains Extract, Transform, Load (ETL) processes, or Application Programming Interfaces. | Multiple systems need pipelines and warehousing at scale. |
| Data Entry / Processing | Reliable, consistent data input; basic cleaning. | Keeps beneficiary and case notes up to date in CRM/Sheets. | High-volume manual data collection. |
| Data Governance / Quality | Maintains policies, quality rules, access controls, and privacy by design. | Introduces retention policy; data dictionary; and DPIAs for new forms. | Handling sensitive data or compliance risks. |
| Data Manager | Sets data strategy, governance, prioritisation; coordinates internal/outsourced work; ensures GDPR. | Owns reporting roadmap; signs off contractor deliverables; introduces data standards. | To build data processes; mostly the first hire, or if you need oversight of volunteers and consultants. |
| Data Scientist | Uses existing data to build models; creates in-depth analyses using coding, Machine Learning, and programming tools. | Builds a model using existing volunteer data to predict churn. | Want to use a wider variety of analyses that you know will be valuable, such as prediction. |
| Database Administrator | Backups, security, performance, upgrades. | Keeps donor CRM healthy; manages access and restorations. | Bespoke, in-house CRM or SQL-based systems are mission-critical. |
Other variants
Analytics Engineer: A less-used term for a type of data engineer that is more focused on solving business problems.
Machine Learning Engineer: Builds data science models, but also has the software engineering skills to put them into production.
Marketing Analyst: focused on understanding and optimising marketing spend, such as Google Ads or email success rates.
Product analytics/Product Data Scientist: Most common in businesses building customer-facing websites or apps, supporting software teams to be more effective.
Reporting Analyst: Focused on creating data outputs such as dashboards.
Structuring your team
Based on your goals and requirements, how many people will an organisation of your size need? What is your current budget for hiring, or development? Will you recruit internally from existing staff who are keen to build their skills and move into these roles?
Small/one person
In a one-person team, you will be asking that person to do two or more roles. Look for people who've done similar roles in small organisations before. Be realistic about how much it's possible to deliver, and clear about which goals you want to prioritise.
Medium/two-four people
A team of this size should act as one department. Most teams need at least one person with a data engineering skillset, and one with a data analyst skillset.
In a two-person team, consider a senior data analyst to set data vision, design a delivery plan, and execute it; and a data engineer to bring all the data into one place and make it easy to use. The data engineer might also need to do database admin work.
In a three-person team, hire two people and then assess where your biggest gap is. Do you need a database admin? Is the cleanup work really costly, meaning you need another data engineer? Do you need another data analyst?
In a four-person team, split the senior analyst role into an analyst role and a manager role.
Large/five+ people
With a team this size, you have options about centralising vs embedding data people in other teams. I'd recommend a hybrid approach, keep the data team managed by one person, but embed them in different teams eg in fundraising, service delivery, and operations. They should act as a member of these teams, but also report to a data lead.
Suggested roles:
Data lead, manager, or head of, depending on team size.
Data analysts: one per area.
Data engineers: approximately one for every three analysts. More if your data is complex.
Database administrator.
Conclusion
You don’t need to hire a one-in-a-million data unicorn who can do everything - you need to find someone with the right skills to deliver the projects and work you have prioritised. By working through the points above, you should be able to outline:
A list of goals
The suitable outcomes and relevant skills you’re looking for
Knowledge of the resources you already have
An understanding of the shape of the data team you need
Then you can start the important part - finding the right people.
