Service 02

The people your data team is missing. In a week, not a quarter.

Data, BI, AI and integration specialists who join your team, work in your tools and report to your lead. Junior to methodologist, from one month, with a senior mentor behind every person.

Discuss your team See how it works

Executive summary

You have a data team and someone who leads it. What you don't have is enough hands, or one specific skill: a Fabric engineer, a Power BI developer who can fix the model everyone complains about, an AI developer to build the first agent, a Python developer to make the integration finally run.

Hiring takes months. Agencies sell you a project when you need a person.

We give you the person. They start within a week, work inside your team and your systems, and stay for as long as you need — from one month. Every specialist we place has a more senior mentor inside Data Never Lies they talk to every week, so you get a middle who works like a senior, and a senior who works like a methodologist. If it isn't working, we replace them.

That's the whole offer. The rest of this page explains how it works.

When this fits

This is for you if:

You have a Head of Data, CIO, CTO or an engineering lead who knows what needs to be built — and simply doesn't have the people to build it.
A project is stuck because one skill is missing: Fabric, dbt, Power BI, Azure Functions, LLM integration, Power Platform.
You need capacity for 1–6 months, and hiring for that makes no sense.
Your data platform, pipelines or migration need an engineer now, not after the next hiring round.
You tried a freelancer and spent more time managing them than it saved.

This is probably not for you if:

Nobody on your side can set tasks and judge the result. Then start with an introduction call — you may need a project or an outsourced function instead, and we'll say so.
You need one small fix, a few hours. Ask us anyway, but a month is our minimum.
You want someone to decide what to build. That's Data Therapy or Process Transformation, not an extra engineer.

Who you can add to your team

Data engineer

Typical work: Warehouses and lakehouses, ETL/ELT pipelines, data quality, migrations (Excel → platform, on-premise → Azure / Fabric, Tableau or Qlik → Power BI)

BI developer

Typical work: Power BI, Tableau, Looker: semantic models, DAX, dashboards people actually open, performance fixes

Analyst

Typical work: Metrics definitions, ad-hoc analysis, reporting that answers the question that was asked

AI developer

Typical work: Assistants and agents on your data, RAG and context layers, Copilot and Claude / OpenAI integrations

ML engineer

Typical work: Forecasting, scoring, anomaly detection — models that run in production, not in a notebook

AI systems architect

Typical work: Designs how AI fits into your data platform, security and workflows before anyone writes code

Forward deployed engineer

Typical work: Sits with your business team, builds and ships the thing they need this month, then the next one

Integration developer

Typical work: APIs, NetSuite / Business Central / Salesforce connections, Azure Functions, event flows

Power Platform developer

Typical work: Power Apps, Power Automate, Dataverse: operational apps on top of Microsoft 365

Python developer

Typical work: Back-ends, services and automation around your data — anything that isn't a dashboard

Project manager

Typical work: Runs the delivery when you add two or more people and want one point of contact

Role	Typical work
Data engineer	Warehouses and lakehouses, ETL/ELT pipelines, data quality, migrations (Excel → platform, on-premise → Azure / Fabric, Tableau or Qlik → Power BI)
BI developer	Power BI, Tableau, Looker: semantic models, DAX, dashboards people actually open, performance fixes
Analyst	Metrics definitions, ad-hoc analysis, reporting that answers the question that was asked
AI developer	Assistants and agents on your data, RAG and context layers, Copilot and Claude / OpenAI integrations
ML engineer	Forecasting, scoring, anomaly detection — models that run in production, not in a notebook
AI systems architect	Designs how AI fits into your data platform, security and workflows before anyone writes code
Forward deployed engineer	Sits with your business team, builds and ships the thing they need this month, then the next one
Integration developer	APIs, NetSuite / Business Central / Salesforce connections, Azure Functions, event flows
Power Platform developer	Power Apps, Power Automate, Dataverse: operational apps on top of Microsoft 365
Python developer	Back-ends, services and automation around your data — anything that isn't a dashboard
Project manager	Runs the delivery when you add two or more people and want one point of contact

Seniority

Junior: executes well-defined tasks under a senior from our side.
Middle: owns a stream of work: a pipeline, a model, an app.
Senior: owns the solution: architecture, trade-offs, the hard parts.
Methodologist: our highest level. Sets the standard: how the platform, the model or the AI layer should be built, reviews everything, teaches your team.

The mentor rule. Whoever you add, there is always a more senior person inside Data Never Lies behind them. A middle has a senior to check with. A senior has a methodologist. You pay for one person and get the judgement of two.

One person in your team, two levels of judgement.

Stacks

Microsoft (Azure, Fabric, Power BI, Power Platform), Snowflake, Databricks, Google Cloud / BigQuery, dbt, Fivetran, Airbyte, Claude, OpenAI, Copilot.

Full list

What you get

A named specialist (or a small team) working inside your tools: your repo, your Jira, your Teams or Slack, your stand-ups.
Start within a week of agreeing the role.
A senior mentor from our side behind every person — included, not an extra.
Reporting to your lead. We don't insert a layer between you and the work. With three or more people we add a project manager if you want one.
Monthly commitment, from one month. Scale up, down or stop with notice.
Replacement if it doesn't work out — same role, no re-selling, no restart fee.
Two working time zones: London and Austin. On-site in London when it helps.
Access to the rest of DNL when you need it: a Data Therapy session for your leadership, a quick architecture review, an AI pilot.

How it works

1
Call — day 1. 30–60 minutes with your lead: what needs building, which stack, who they'd report to, for how long.
2
Match — days 2–3. We propose one or two people with a short profile: stack, seniority, what they built last. You talk to them. No CVs in bulk.
3
Start — within a week. Access, onboarding, first tasks. The mentor from our side joins the first week's calls.
4
Work — weekly rhythm. The person works in your process. Once a week we check in with you and with them: is the work right, is the pace right, what's blocking.
5
Review — monthly. Keep going, change the role, add someone, or stop.

What we need from you: a lead who sets tasks, access on day one, 30 minutes a week.

Examples

A UK fire & security contractor — operational layer on top of NetSuite.

A group of three companies ran projects in NetSuite and spreadsheets around it. They needed engineers to build an operational portal — project tracking, cost-to-complete, purchase orders, revenue forecast, handover — connected to NetSuite. Our team of a senior full-stack engineer, an integration developer and a data engineer joined their operations director's team. Stack: React, Azure Functions, Azure SQL, NetSuite API, Azure DevOps. Six months in, the first phase is live and used daily; the next phase covers contracts, a data warehouse and AI agents on top.

A large UK manufacturer — Microsoft AI layer from the data up.

The company had Microsoft 365 and Copilot licences and almost no use of them. We added a data engineer, an AI developer and an AI systems architect to their IT team. Together we built the data infrastructure and ETL pipelines, a context layer over company data, the first AI agents, and ran Copilot training for the business teams. The client's team now runs it; we stay on for new agents.

Three months to move off Power BI.

A client's data team decided to replace its Power BI reports with custom-built dashboards, and didn't have the hands to do it alongside daily work. A BI developer joined them for three months: mapped the existing models and measures, migrated the data logic, rebuilt the reports as custom dashboards and handed over the documentation. The in-house team now runs and extends them.

Tools and platforms

Microsoft Azure / FabricMost of our placements are on the Microsoft stack — data platforms, pipelines, security
Power BIOur primary BI platform; semantic models, DAX, migrations from Tableau and Qlik
Power PlatformOperational apps and automation on top of Microsoft 365
Snowflake / Databricks / BigQueryFor clients already on a lakehouse or Google stack
dbt · Fivetran · AirbyteModelling and connectors — the standard toolkit for our data engineers
Anthropic / Claude · OpenAI · CopilotWhat our AI developers build with, connected to your data
Azure DevOps · GitHubWhere the code lives — yours, not ours

All technologies we work with

Formats and commercials

Monthly rate per person, by role and seniority. Minimum one month. Rates on request — tell us the role and we'll reply the same day.
Part-time possible for senior and methodologist roles (for example two days a week for architecture and reviews).
Team (3+ people) — we add a project manager and a single monthly invoice.
Contracts are with Data Never Lies LTD, England and Wales. The specialist works under your direction; IP in what they build is yours.

Who does the work

Technology squads inside Data Never Lies, each specialised in a stack — Microsoft, Databricks, Snowflake, Google. Head office in London, engineering hubs in Barcelona and Astana, business development in Austin and Sydney. Working time zones: London and Austin.

Questions

How fast can someone really start?

Within a week of agreeing the role, usually faster for Microsoft and Power BI roles. The limiting factor is normally access on your side.

What if the person doesn't fit?

Tell us. We replace them with the same role and seniority, on terms we agree with you up front, and the mentor who already knows the context helps the new person pick up quickly.

Who manages them?

Your lead. We don't add a layer. Our mentor is there for the specialist, not between you and them.

Can we hire them permanently later?

Yes, that's possible. We agree the terms with you up front.

Do you work in our tools and repo?

Yes. Your repo, your tickets, your chat. The code and the IP are yours.

Can we start with one person and grow?

That's the usual way. Most clients start with one role and add the second when the first one is clearly working.

What about security and NDAs?

NDA before any access. We work under your security policies, on your tenant, with the accounts you issue.

Tell us which role you're missing.

Discuss your team

A call within one working day. A proposed profile within three.