Upskilling & Enablement

A new platform is only as good as the number of people who can use it properly. Without training, engineers copy whatever they find in the repository, analysts build their own versions of the same table, and the standards agreed during the project fade within months. Generic courses do not help much, because they teach a tool in the abstract. We teach on your platform, using your code and your data, and we leave behind the standards and examples to keep it consistent.

Taught on your own platform

Labs run in your environment, on your own pipelines and tables, so the lessons apply the next morning.

Standards your team agrees on

We help your engineers write down how things should be done, then teach those standards to everyone else.

Learning through real work

Our engineers pair with yours on actual tickets, which teaches more than a classroom ever does.

Skills assessment

A short review of each group’s experience and gaps, so teaching starts at the right level.

Hands-on engineering labs

Building, testing, and deploying real pipelines and infrastructure, with code review from our engineers.

Analyst and self-service training

Using the governed model well, writing reliable queries, and building reports that others can trust.

Engineering standards and playbooks

Written conventions for naming, testing, review, and release, created with your team and kept in your repository.

Pairing and coaching

Our engineers work beside yours for a few weeks on live tasks, then step back as confidence grows.

Data ownership and governance training

Helping data owners and stewards understand what they are responsible for and how to do it well.

• Data engineers

  What we teach

  Building and testing pipelines, writing maintainable models, handling failures, and releasing changes safely.

  What they can do afterwards

  Deliver and support a new pipeline end to end using the team’s own standards.

• Analysts and BI developers

  What we teach

  Working from the governed model, writing efficient queries, and designing reports for decisions.

  What they can do afterwards

  Answer questions from trusted data without building a separate copy.

• Platform administrators

  What we teach

  Capacity and cost control, workspace and access setup, monitoring, and release practice on Fabric or Databricks.

  What they can do afterwards

  Run the platform from code and respond to common incidents.

• Data owners and stewards

  What we teach

  What ownership means, how to define a metric, and how to handle quality problems and access requests.

  What they can do afterwards

  Take responsibility for their data and keep its definitions current.

• Leaders and managers

  What we teach

  How to read cost and quality reports, judge proposals, and set priorities for data work.

  What they can do afterwards

  Ask better questions and sponsor the right work with confidence.

1. Phase 1

   1 week

   Assess

   • Survey and interview a cross-section of the people to be trained
   • Review your platform, repositories, and current standards
   • Agree what good looks like and how it will be measured

   You receive

   Skills findings, a programme outline, and baseline measures.

2. Phase 2

   2 to 3 weeks

   Design

   • Build labs from your own pipelines, tables, and infrastructure
   • Draft the engineering standards and playbooks with your senior engineers
   • Test the first session with a small group

   You receive

   Course materials, labs, and draft standards.

3. Phase 3

   4 to 8 weeks

   Teach and pair

   • Run a pilot cohort, then adjust before widening
   • Deliver workshops and labs by role
   • Pair with engineers on real tickets and review their work

   You receive

   Trained cohorts and a body of reviewed, real work.

4. Phase 4

   Ongoing

   Embed

   • Name internal leads to coach new joiners
   • Measure results against the baseline
   • Update the standards as the platform changes

   You receive

   Results report, internal leads in place, and refreshed standards.

What you keep

Materials you can reuse

Slides, labs, and playbooks are yours to keep, edit, and use with new joiners.

Practice on safe data

Labs run on test or masked data, so nobody has to work with anything sensitive.

Standards in your repository

Conventions and examples live alongside your code, where your engineers already look.

Internal leads trained

A few of your people learn to coach others, so the practice continues without us.

Clear measures

Before and after results for review quality, incident rates, and confidence, so the value is visible.

Tools we teach with

Data platforms

Microsoft Fabric, Databricks, Snowflake, taught on whichever you run

Pipelines

Fabric Data Factory, Databricks Workflows, Azure Data Factory, Airflow

Transformation

dbt, SQL, Python, Spark, with testing and review practice

Delivery practice

Git workflows, CI/CD, code review, release checks

BI tools

Power BI, Tableau, Looker, Metabase

Learning delivery

In-person workshops, live online sessions, and recorded labs

We teach on the platforms and tools your company has already chosen, not ours.