Mentoring for data engineers and data scientists

The data job market is big and confusing: the same titles cover very different roles. I help you choose where to position yourself, present yourself well, and strengthen the part that is most often missing: data in production on the cloud.

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Who it is for

Data analysts who want to move into data engineering or data science
Data engineers who want to grow on cloud platforms and data architecture
Data scientists who want to take models to production (MLOps)
Developers and DBAs moving into data
STEM graduates who want to enter the field

What we work on

1

Choosing the role

Data analyst, analytics engineer, data engineer, data scientist, ML engineer: real differences, what companies ask for and where you fit best.

2

Cloud data platforms

Data services on AWS, Azure and GCP, data lakes and data warehouses, security and costs.

3

Pipelines and orchestration

ETL and ELT, scheduling, data quality, data infrastructure managed as code.

4

MLOps and production

Containers, CI/CD for models, versioning, monitoring: getting a model out of the notebook.

5

Resume, LinkedIn and portfolio

How to describe data projects so that both a recruiter and an engineer understand them.

6

Interviews

SQL, pipeline design, questions about your past work, in English or Italian.

What I don't do

I am not a machine learning researcher. If you need to go deep into the maths of models or a specific ML technique, I will tell you and point you to better-suited resources or people. My contribution is career, architecture, cloud and production: the part that often separates a good data profile from one that gets hired.

How you can work with me

Mentoring package · 3 sessions

Three 60-minute sessions on a concrete goal. Price on request.

Ask about the mentoring package

Sprint and Elite programs

Structured programs of 3 or 6 sessions, with materials and support between sessions.

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Carlo Sacchi, IT Coach and Cloud Architect

Who you work with

I am Carlo Sacchi, Cloud Architect and IT coach with more than 20 years of experience on production systems and multiple Azure and AWS certifications. You talk directly to me, from the free call to the last session. And if I think you don't need a coach, I will tell you.

About me →

Frequently asked questions

It depends on what you enjoy. A data engineer builds the systems that move and prepare data; a data scientist analyzes it and builds models on it. In the free call we work out which one is closer to your profile.

For many data engineering roles, what matters most is building systems that work. For data scientist roles many companies ask for a quantitative background: we take that into account when choosing the path.

Yes, especially with those who want to grow towards more technical roles such as analytics engineer or data engineer.

Yes: resume, LinkedIn and portfolio are often the first thing we work on, because data job titles are confusing and you need to explain clearly what you can do.