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Bootcamp · Self-paced or mentor-led

Own the data platform.

A hands-on program that takes engineers from medallion notebook to operating a production-grade twenty-service enterprise data platform. Self-paced forever. Mentor-led when you want live feedback.

Enroll in bootcamp

Still deciding? Ask first.

Message a mentor about fit, prerequisites, or where to start. Replies come on WhatsApp, usually within a day.

  • Curriculum fit, prerequisites, or where to start
  • Honest answer, no pressure to enroll

Taught by an engineer who has shipped this

  • ISO 27001

    Led the engineering work behind the certification of a regulated EU platform.

  • Series A platform

    Architected the case-management product that became the business a €11.6M round was raised on.

  • 3x faster deploys

    Cut time-to-deploy by migrating to Kubernetes on GCP with deploy-on-merge.

Data engineering is backend engineering with a warehouse, an orchestrator, and a quality gate. The bootcamp teaches the production shape most tutorials skip: medallion warehouses, Airflow DAGs with real retries, Spark transforms that do not tangle batch and streaming, FastAPI control planes that degrade gracefully, and governance wired into the platform instead of bolted on after.

By the final phase you will operate a twenty-service reference platform end to end and you will be able to point a new hire at the repo on day one. Self-paced learners keep lifetime access to every phase. Mentor-led cohorts add live Q&A, code review, and a Slack channel where engineers ship together.

Curriculum

Ship a platform, not a pile of scripts.

  1. 01

    Medallion warehouse on a laptop

    Take a real dataset from raw to BI marts using bronze, silver, gold in DuckDB and pandas. Quality gates that catch drift before gold updates.

  2. 02

    Airflow DAGs with real retries

    Turn the notebook into a scheduled pipeline. Partitioned parquet in MinIO, task-level retries, idempotent backfills across date ranges.

  3. 03

    Spark, Postgres, and Great Expectations

    Write the Spark batch job that loads a Postgres star schema. Gate the transform on a Great Expectations suite so bad data never reaches gold.

  4. 04

    FastAPI control plane with typed config

    Build a typed FastAPI service with pydantic-settings, async health checks, and a /pipeline/trigger endpoint. Smoke test proves graceful degradation.

  5. 05

    Streaming, ML, and governance

    Add Kafka producers and consumers alongside the batch track. Wire MLflow into an Airflow task. Register Atlas lineage. Scrape Prometheus and render Grafana.

  6. 06

    Port .NET to Python and deploy

    Port a .NET 8 FastAPI control plane to Python 1:1 preserving contracts. Wrap the stack in Helm charts. Provision the infra with Terraform.

Outcomes

You finish able to:

  • Model a real dataset into bronze, silver, gold and defend every layer on a whiteboard
  • Wire Airflow with retries, partitioned parquet, and one-click backfills
  • Gate the warehouse on Great Expectations so bad data never hits the dashboard
  • Ship a typed FastAPI control plane with async health checks that degrade gracefully
  • Extend the platform with Kafka streaming without tangling the batch track
  • Register Atlas lineage and explain provenance to a compliance auditor in minutes
  • Port a .NET 8 backend to Python 1:1 while preserving every HTTP contract
  • Deploy the stack to Kubernetes with Helm charts and Terraform modules

Who it's for

Is this for you?

Backend engineers

asked to own the data pipeline without a real reference implementation to ground the work

Analytics engineers

maintaining a growing pile of cron jobs and ready to move to real orchestration

Senior engineers

moving into a data platform role and wanting the full enterprise toolchain without months of trial and error

AI engineers

wiring data ingestion for RAG and features and realizing cron plus bash cannot scale

Pricing

Invest in your data engineering career.

A fraction of a data engineer's monthly salary. Lifetime access to all content.

FAQ

Frequently Asked Questions

Do I need a real cluster or cloud credits?
No. Everything runs on docker-compose on a laptop. The bootcamp shows the Kubernetes, Helm, and Terraform shapes so the port to a managed cluster is mostly mechanical.
How is this different from a Databricks or Snowflake course?
Those teach the managed platform. We teach the unbundled shape first, so you understand what the managed platform is replacing. After this, reading Databricks docs is easy.
Do I need prior data engineering experience?
No. The bootcamp starts from a notebook and builds up. Python comfort and SQL basics are enough. By week six you will operate a reference enterprise data platform.
Can I skip straight to the production tier?
You can buy any tier individually. The bootcamp exists because the three courses reinforce each other. The medallion notebook sets up the warehouse model, the pipeline course puts it on a schedule, and the enterprise course scales the shape.

Own the platform.

Pick self-paced to start today or mentor-led for live feedback alongside Param.

Enroll in bootcamp

Data engineering bootcamp

Mentor-led + self-paced

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