Courses / Live bootcamps
Data Engineering with AI Bootcamp
Pipelines, warehouses and quality checks, with agents doing the grunt work.
- weeks, live on Zoom
- 12
- hours a week
- 15
- deployed projects
- 6
- minutes 1-1, every week
- 30
Who it is for, and what you receive.
Engineers with SQL and Python and some backend or analytics experience who want to build the pipelines, warehouses and quality layers that AI systems eat from.
Before you start
SQL and Python; some backend or analytics experience.
Skills
- SQL
- Python
- orchestration
- dbt
- streaming
- data contracts
- cloud
A week, about 15 hours
- Live on Zoom
- 4h
- Recorded lessons and exercises
- 6h
- The project
- 5h
- 1
Six deployed projects
Each graded by Nova against a published rubric and signed off by the instructor at the gate.
- 2
The recorded lessons
The track's lessons and exercises, graded line by line, open for twelve months after the cohort ends.
- 3
Live on Zoom every week
A 90-minute live code review, a 60-minute squad lab, 60 minutes of office hours, and a 30-minute 1-1 with your instructor.
- 4
Your hiring plan
Opened in week 1 and reviewed in every 1-1: target roles, the gap map, the proof list, weekly actions, an interview log, the outcome.
- 5
The record
Every grade and every project with its repository at square1ai.com/u/{handle}, and the recording of your viva, verifiable by any employer.
- 6
The certificate
A credential ID that resolves at square1ai.com/verify to the real completion.
- 7
The hiring sprint
Weeks 11 and 12: CV and portfolio from graded work, applications, mock interviews scored against the role, demo day.
- 8
Nova for twelve weeks
A tutor with every submission of yours in its memory, at 2am as well as in class.
Twelve weeks in six blocks.
Each block teaches for a week, then you build and deploy a project, then a gate checks it before the next block opens. The project and its gate are the block's record entry.
Live time each week on Zoom: a 90-minute class where the instructor reviews real submissions, a 60-minute squad lab, 60 minutes of office hours, and your own 30-minute 1-1. Nothing is lectured live; the recorded lessons do that.
- 1
Pipelines
Weeks 1 to 2Week 1
Pipelines
- Batch and streaming, when each
- Orchestration
- Idempotency and backfills
You build: An orchestrated pipeline
Week 2
Project 1: backfill
- Scheduling
- Identical reruns
You build: A pipeline with backfill, running on a schedule
Project 1, the gate at week 2
Backfill
An orchestrated pipeline whose backfill produces exactly what the original run did.
You hand in
- Pipeline
- Orchestration
- Backfill
- Schedule
The gate
A backfill produces identical output to the original run.
- 2
Warehouses
Weeks 3 to 4Week 3
Warehouses
- Modelling for analytics
- dbt-style transformations
- Partitioning and cost
You build: A modelled warehouse
Week 4
Project 2: the warehouse
- Tests per model
- Documentation
You build: A modelled warehouse with tested transformations
Project 2, the gate at week 4
The warehouse
Model a warehouse for a real business with tested, documented transformations.
You hand in
- Models
- Tests
- Documentation
- Cost notes
The gate
Every model has a test and documentation.
- 3
Quality
Weeks 5 to 6Week 5
Quality
- Data contracts
- Anomaly detection on data
- Lineage
You build: Quality layer; squads form
Week 6
Project 3: contracts
- Blocking downstream runs
- Alerts
You build: A data-quality layer with contracts and alerts
Project 3, the gate at week 6
Contracts
Refuse bad data before it spreads.
You hand in
- Data contracts
- Anomaly detection
- Lineage
- Alerts
The gate
A contract violation blocks downstream runs.
- 4
Agents for data work
Weeks 7 to 8Week 7
Agents for data work
- LLMs writing and reviewing SQL
- Agents for migrations and documentation
- Where they go wrong
You build: A documentation agent
Week 8
Project 4: the agent, checked
- Review before landing
- Mistake logs
You build: An agent that documents and reviews a warehouse, with its mistakes logged
Project 4, the gate at week 8
The agent, checked
Let an agent document and review the warehouse, and catch its mistakes.
You hand in
- Agent
- Review gate
- Mistake log
The gate
Agent output is checked before it lands.
- 5
Platform
Weeks 9 to 10Week 9
Platform
- Streaming at scale
- Cost and governance
- Serving data to ML and LLM systems
You build: Platform design
Week 10
Project 5 and the viva
- Serving an ML feature and a RAG index
- Defending lineage
You build: A data platform serving an ML feature and a retrieval index; the viva
Project 5, the gate at week 10
The platform
Serve an ML feature and a retrieval index from one platform, monitored.
You hand in
- Streaming
- Feature serving
- Index feed
- Governance
- Monitoring
The gate
The viva: defend the platform and its lineage.
- 6
Employer brief and hiring sprint
Weeks 11 to 12Week 11
Employer brief
- A real problem from a hiring partner, in squads
- A partner's data problem
- Working to someone else's definition of done
You build: The employer brief in progress
Week 12
Hiring sprint
- CV and portfolio built from graded work
- Applications and follow-ups in the hiring plan
- Mock interviews scored against the role
- Demo day
You build: Project 6 delivered; demo day
Project 6, the gate at week 12
Employer brief
A partner's data problem.
You hand in
- The brief delivered
- Demo day
The gate
The brief's owner accepts the result.
What you can do by week 12.
Six deployed pipelines and platform pieces, with the contracts and lineage that make a data platform something an AI team can build on.
- 1
Build orchestrated batch and streaming pipelines with idempotency and backfills.
- 2
Model a warehouse for analytics with tested, documented transformations.
- 3
Build a data-quality layer with contracts, anomaly detection, lineage and alerts.
- 4
Use agents for SQL, migrations and documentation, and check their work before it lands.
- 5
Run a data platform that serves an ML feature and a retrieval index.
- 6
Defend a platform and its lineage.
Roles this prepares you for
- Data engineer
- Analytics engineer (senior)
- ML platform engineer
- Data platform engineer
No placement rate is shown, because there are no graduates to count yet. The roles above are what the projects are built for.
Your record at week 12.
Every exercise and project is graded by Nova against a rubric you can read, and every grade is kept on one page an employer can open and run. This is what the programme writes to it.
- Graded, line by line
- Nova reads every submission against the brief and the rubric and returns a score, what you did well and what to fix.
- Six gates
- A block does not open until the previous project passes its gate. You always know where you are and what is next.
- A weekly 1-1
- Thirty minutes with your instructor, who has already read your code before the call.
- One page an employer can run
- Every grade, project and the viva recording at /verify. An employer opens it, runs the code and watches you defend it.
Record, Data Engineering with AI Bootcamp
Example
- Week 2Graded, gate signed
Backfill
A backfill produces identical output to the original run
- Week 4Graded, gate signed
The warehouse
Every model has a test and documentation
- Week 6Graded, gate signed
Contracts
A contract violation blocks downstream runs
- Week 8Graded, gate signed
The agent, checked
Agent output is checked before it lands
- Week 10Graded, gate signed
The platform
A viva: defend the platform and its lineage
- Week 12Graded, gate signed
Employer brief
The brief's owner accepts the result
- Weeks 1 to 12Kept
Twelve 1-1 notes
What your instructor saw in your work each week, and what you agreed to do next.
- Week 12Kept
Your hiring plan and its outcome
Target roles, the gap map, applications, interviews and where you landed.
The entries, not the grades: those are yours to earn. The page lives at /verify and an employer needs no account to open it.
8 entries, twelve weeks. One email when applications open.
How we help you find a job.
The last block is not curriculum. It is the hiring sprint, and the proof you built in the ten weeks before it.
- 1
Your hiring plan, from week 1
Six parts you and your instructor keep: target roles, the gap map from real postings, the proof to send, weekly actions, an interview log, the outcome. Read before every 1-1.
- 2
The hiring sprint
Weeks 11 and 12: CV, portfolio, applications, mock interviews, and demo day in front of hiring partners.
- 3
A record an employer can run
Your six deployed projects and the recorded viva on /verify. An employer opens it, runs the code and watches you defend it.
- 4
The career agent
Paste a real job posting at /career and it maps the role to your graded work and what to do next.
- 5
The roles directory
Every role we prepare people for, what it pays and what it asks, at /roles.
One email when applications open.
Fifty seats, one instructor, twelve weeks. The waitlist hears the date and the price first, and nothing is charged before the cohort exists.