Data Engineering for Analysts

What This Course Does?

This course teaches analysts to fetch, store, transform and automate data using production-grade data engineering tools — and build the pipelines that feed their analysis.

Data engineering is the foundation that makes analysis possible. Engineers design and build the systems that move, store, and structure data at scale. Analysts work with what those systems produce. That division of labour works — until the pace of business demands more. Analysts who understand the pipeline, not just the output, move faster, ask better questions, and deliver more independently.

This course gives you that foundation. You will learn to fetch live data from APIs, store and query it in BigQuery, transform it using dbt, visualise it in Looker Studio, and schedule the entire pipeline to run automatically. These are not peripheral skills — they are the layer of the data engineering stack that now sits squarely in analyst territory.

Who Is This For?

Data analysts and analytics professionals who are comfortable with SQL and Python and want to own more of the data lifecycle — from source to insight.

This course is for data analysts and those who have studied data analytics and are hitting a ceiling. You are comfortable with SQL, Python, and visualisation. You can analyse data well. But the data you work with arrives pre-prepared — someone else fetches it, cleans it, and loads it. You are dependent on that process, and it limits what you can do and how fast you can do it.

This course is for analysts who want to close that gap: to understand where data comes from, build the pipelines that produce it, and own the full workflow from source to insight.

You will get the most from this course if you are already comfortable with:

Skill	Why it matters here
SQL	BigQuery and dbt are SQL-first. You will write queries from lesson one.
Python (basic)	API calls, data cleaning, and pipeline scripts are all written in Python.
Data analysis fundamentals	You should understand what clean, analysis-ready data looks like — this course focuses on how to produce it.

Where Does This Course Sit in the Data Engineering Landscape?

The data engineering stack spans enterprise infrastructure to analyst-facing pipelines. This course covers the layer that is directly relevant to analyst work: ingestion, cloud storage, transformation, and orchestration.

Data engineering is a broad discipline. It spans everything from real-time event streaming at enterprise scale to the lightweight pipelines that feed a team’s weekly reporting. Not all of it is relevant to every role — and understanding where analyst work sits within that landscape is part of what this course teaches.

ETL, data governance, and cloud skills now appear as valued requirements in data analyst job postings, with cloud platform mentions in analyst roles rising roughly 3% year-on-year in 2025. Analysis of data science job postings shows SQL and pipeline skills in the majority of postings — 79% and 31% respectively — with dbt up 9 percentage points year-on-year. The expectation is clear: analysts and data scientists are being asked to work directly with data infrastructure, not just consume clean tables.

The “full-stack data analyst” is an emerging category — professionals who understand how data flows through cloud platforms and pipelines before reaching dashboards, making them significantly more valuable to employers. At the same time, the data engineering stack is large, and not all of it belongs in analyst work. The table below maps the full landscape and shows where this course fits within it.

Tool / Layer	What it solves	Typical user	In this course?
Apache Spark	Distributed processing of petabyte-scale data across server clusters	Data engineers at organisations processing billions of events. Requires Scala or Java.	No — enterprise scale
Apache Kafka	Real-time event streaming at millions of events per second	Engineering teams building live transaction and monitoring systems.	No — real-time systems
Kubernetes	Container orchestration for managing cloud infrastructure at scale	DevOps and platform engineering teams.	No — infrastructure ops
REST APIs + Python	Fetching structured data from external sources programmatically	Analysts and engineers. Standard across public sector, finance, consulting.	✓ Lessons 1–4
BigQuery	Cloud-scale SQL data warehouse — query millions of rows without a server	Analysts and engineers. Widely used in public sector and enterprise analytics.	✓ Lessons 5–8
dbt	SQL-based transformation layer — clean, version-controlled, analysis-ready models	Analytics engineers and senior analysts. Up 9pp year-on-year.	✓ Lesson 7
Prefect	Pipeline orchestration — scheduling, retries, monitoring	Analysts and engineers building automated workflows. Python-native, free cloud tier.	✓ Lessons 10–12

ETL skills were cited in over 9% of data analyst job postings in 2024, a figure that has continued to rise as organisations expect analysts to handle more of the data lifecycle. This course covers exactly that layer: ingestion, cloud storage, transformation, and orchestration. The tools are production-grade, the skills are immediately employable, and the project you build is something you can speak to in any interview.

Curriculum

One module, twelve lessons, three phases — a single continuous project from live API source to a fully deployed, automated pipeline.

By the end of this course you will have built a Python script that fetches live data from a public API, validates and cleans it, loads it into BigQuery, transforms it using dbt, visualises it in a Looker Studio dashboard, and runs automatically on a schedule. That is a project you can describe in two sentences in any interview.

Everything runs in Google Colab — no local setup, no configuration. The full stack is free at the scale this course requires.

Tool	Purpose	Cost
Google Colab	Coding environment	Free
Google BigQuery	Cloud data warehouse	Free tier: 1TB queries/month, 10GB storage
dbt Cloud	SQL transformation	Free Developer account
Prefect	Orchestration and scheduling	Free Cloud tier
Looker Studio	Live dashboard	Free
Google Cloud Scheduler	Automated deployment	Free tier

#	Lesson	What you build
01	What Is a Data Pipeline?	Mental model: raw → stored → consumed
02	Working with APIs	REST, JSON, authentication — first live API call
03	Paginating, Cleaning and Validating API Data	Loops, nested JSON, nulls, schema checks
04	Your First Ingestion Script	End-to-end reusable fetcher saved to Google Drive

#	Lesson	What you build
05	From CSV to BigQuery	Why pandas breaks at scale — cloud storage intro
06	Loading and Managing Data in BigQuery	Schema, append vs overwrite, versioning
07	Transforming Data with dbt	SQL models, version control, analysis-ready tables
08	Querying BigQuery from Colab	Python client, parameterised queries
09	Building a Dashboard with Looker Studio	Live output connected directly to BigQuery

#	Lesson	What you build
10	What Is Orchestration?	DAGs, tasks, flows — mental model and Prefect intro
11	Scheduling and Error Handling	Cron, retries, alerts — pipeline resilience
12	Deploying Your Pipeline	Cloud Scheduler — runs without you

What Will You Develop?

Technical skills, business judgement, AI literacy, and a portfolio of tangible deliverables — built into every lesson.

Every lesson in this course is built around four dimensions of professional growth — not just technical skill.

Technical Skills	APIs, BigQuery, dbt, Prefect, Cloud Scheduler — the actual tools employers expect.
Business Acumen	Every lesson opens with a business scenario. Every code example answers a real question. Syntax is never taught in isolation.
AI Literacy	AI tools like Gemini is integrated throughout — for code generation, debugging, and pipeline documentation. You learn to use AI as a tool, not a crutch.
Personal Branding	When a lesson produces something tangible — a working pipeline, a BigQuery dataset, a deployed scheduler — you are shown exactly how to describe it. Not “I learned about X.” “I built X that does Y.”

Program Details

Everything you need to know before you enrol.

Key Info	What you need to know
Duration	5 weeks
Format	Online — self-paced with mentor support. 5 mentor calls + unlimited async support.
Price	€499 (+ 19% VAT in EU)
Prerequisites	SQL, basic Python, and data analysis fundamentals.
Seats	Limited. Application only.