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Data & Databases Intermediate

The Analytics Engineering Track: Pipelines, dbt and Warehouses

Build a warehouse layer with tested transformations that analysts and executives both trust.

8 students

SN Created by Sofia Navarro

  • Last updated August 2026
  • English
  • 16h 20m of material
  • 122 lessons

What you will learn

8 concrete outcomes

Every bullet below is something you will have built, shipped or be able to explain by the time you finish the last lesson.

  • Load raw data into a warehouse without transforming away your escape hatch
  • Layer staging, intermediate and mart models with clear responsibilities
  • Use dbt materialisations and incremental strategies including late data
  • Capture slowly changing dimensions with snapshots
  • Write schema, singular and freshness tests that catch real breakage
  • Define metrics once so two dashboards cannot disagree
  • Control cost on a consumption-priced warehouse
  • Handle the incident where a number was wrong for three days

Course curriculum

8 modules · 122 lessons · 16h 20m of material

15 lessons running 1h 58m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

15 lessons running 2h 4m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

16 lessons running 2h 8m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

16 lessons running 2h 10m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

15 lessons running 2h 4m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

15 lessons running 2h 2m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

15 lessons running 2h in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

15 lessons running 1h 54m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

8 modules · 122 lessons

16h 20m total length

Requirements

Short list, and deliberately so. If you meet these you can start today.

  • Strong SQL including joins, aggregation and window functions
  • Comfortable with Git, the command line and pull request workflow
  • Docker for the local warehouse and orchestration environment
  • Basic Python for the orchestration and ingestion sections

About this course

Analytics engineering is the discipline that appeared when companies realised their dashboards disagreed with each other. It sits between raw data and the people asking questions, and it is mostly software engineering applied to SQL: version control, testing, documentation, review and deployment.

This track builds a complete warehouse layer from raw event and application data. You build staging models that rename and cast without changing meaning, intermediate models that join and enrich, and mart models shaped for the questions a business actually asks.

dbt is the tool throughout, and you learn it properly: models and materialisations, incremental strategies including late-arriving data, snapshots for slowly changing dimensions, seeds, macros that stay readable, and package management. Testing gets serious treatment, with schema tests for structure, singular tests for business rules, freshness checks on sources, and a continuous integration pipeline that runs the whole suite on every pull request.

The final part is what separates a working warehouse from a trusted one: a metrics layer so a definition lives in one place, documentation and lineage that answer where a number came from, cost control on a consumption-priced warehouse, and an incident process for the morning a dashboard is wrong and everyone finds out at once.

Topics covered

Frequently asked questions

Still unsure about something? Write to misteryjj100@gmail.com and a human answers, usually the same working day.

The local environment runs PostgreSQL and DuckDB so nothing costs money, with dedicated lessons on the differences you will meet on BigQuery and Snowflake.

No. Many buyers are the only person doing this work at their company, and the track is structured so one person can build and operate the whole layer.

Yes. Scheduling, dependencies, retries and alerting are covered with a lightweight orchestrator, with notes on migrating to a managed service later.

Checkout is handled on our provider's secure payment page. The moment your payment clears we email your personal access link and access code to the address you used at checkout, and the same link appears in your account library. There is nothing to install and nothing to wait for.

Email misteryjj100@gmail.com within 14 days of your purchase, quote your order number, and we refund the full amount to your original payment method. No form to fill in and no questions about how much of the course you watched.

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Your instructor

SN

Sofia Navarro

Python and data engineer

  • 231 students taught
  • 11 courses published
  • 4.5 instructor rating
  • Python
  • SQL
  • PostgreSQL
  • Data engineering

Sofia builds the pipelines that keep analytics teams honest, mostly Python on top of PostgreSQL under uncomfortable load. She teaches schema design, indexing and query planning against anonymised production datasets rather than toy tables, then wires the same data into pandas so the SQL and the Python halves stop being separate skills. Expect to spend real time reading execution plans and rather less time reading slides. She consults on migrations for teams that cannot afford downtime.

See every course by Sofia Navarro
$139 USD

One-time payment · lifetime access

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