Many mid-sized companies are moving their reporting, pipelines and analytics onto one platform from a single vendor. The pitch is simple: fewer tools, fewer hand-offs, faster answers. The staffing question gets less attention. Consolidating tools does not remove the need for people. It changes which people you need and in what order.
What consolidation actually changes
When data work was spread across separate systems, companies often had one person who knew each piece. One person maintained the warehouse, another ran the nightly loads, a third built the reports in a spreadsheet nobody else touched.
A unified platform puts storage, pipelines, modelling and dashboards in one place. That helps, but it also means the skills overlap more. The person building a pipeline needs to understand how the report will be used. The analyst needs to know where the data came from. Narrow specialists can struggle, and generalists with solid fundamentals become more valuable.
The roles that matter most
Before you buy licences or sign a migration plan, look at these four jobs.
- Data engineer. Builds and maintains the pipelines that move data from your ERP, CRM, plant systems and finance tools into one place. This is usually the first hire and the hardest to fill.
- Analytics engineer or BI developer. Turns clean data into models and dashboards people actually use. Good ones sit close to the business and ask what decision a report supports.
- Data analyst with domain knowledge. In a manufacturer, that is someone who understands scrap, downtime and labour cost. In a distributor, it is margin and inventory turns. Domain knowledge beats tool knowledge here.
- Data governance or ownership lead. Often a part-time duty for a controller or operations manager. Someone must decide which numbers are the official ones.
Machine learning specialists come later for most companies. If your data is not reliable and your reports are not trusted, a model will not fix that.
AI assistants do not replace the team
AI assistants built into data platforms can draft a report, suggest a query or help build a pipeline from a plain-language request. They save time on routine work, and junior staff can get further faster.
They also produce confident output that can be wrong. Someone has to check whether the logic is sound, whether the joins are right and whether the result matches what finance reports. That review takes experience. If you hire only junior people and rely on the assistant to fill the gap, you will ship errors faster.
A practical split looks like this:
- Let the assistant handle first drafts, documentation and repetitive formatting.
- Keep a senior person responsible for review and sign-off.
- Test outputs against a known number, such as month-end revenue or units shipped, before anyone relies on them.
Full-time, contract or both
Platform migrations come in a burst. You need heavy engineering for 3 to 9 months, then much less. Many employers do better with a mix.
- Contract for the build. A senior data engineer or architect on a defined project gets the foundation in place without a permanent headcount you may not need afterward.
- Permanent for ownership. Hire the person who will run the platform, answer questions from finance and operations, and keep the pipelines healthy.
- Contract for peaks. Year-end reporting, an acquisition or an ERP change can justify short-term help.
The risk with contract-only is that the knowledge leaves when the contract ends. Require documentation and a hand-over to a permanent employee as part of the scope.
Questions to ask before you hire
- What 5 reports or decisions would change how we run the business if they were accurate and current?
- Who owns each source system, and will they give access?
- Who signs off on a number before it reaches the leadership team?
- Do we need someone who can build, someone who can interpret, or both?
- If the person we hire leaves in 18 months, can someone else keep it running?
Candidates in this field will be asking their own questions. Strong data people want to know the data is accessible, the sponsor is senior and the work will be used. Vague mandates and tool-first projects are a common reason good candidates decline.
What to do next
Start with the decisions, not the platform. List the reports your leaders rely on, decide who owns the numbers, and then define the first one or two roles you need. Write the job around the outcome, such as one trusted monthly reporting pack, rather than a list of product names.
If you are planning a data or analytics build in Canada or the US, a Traverse recruiter can help you scope the roles and find contract or permanent people who fit.