Data Engineering Mastery Mentorship
Budget: $750 – $1,500 USD
I have a solid grasp of the fundamentals and now want to reach a true senior-level command of data engineering within the next three months. The areas I need to deep-dive into are Data Warehousing, ETL, and complete Data Pipeline Architecture. All work, demos, and code should centre on Python—feel free to reference SQL or Scala when illustrating best practice, but Python must remain the primary language for every hands-on exercise.
Here’s how I picture the collaboration:
• A structured roadmap that breaks the three domains into progressive weekly objectives, each paired with practical tasks I can build out in my own environment.
• Live, interactive sessions for concept walkthroughs, white-boarding architecture, and code reviews. Recordings or detailed notes after each session are a must so I can revisit the material.
• At least two end-to-end mini-projects: one warehousing/ETL build and one real-time or batch pipeline, both deployable locally or on a cloud sandbox. These should incorporate version control, unit testing, and monitoring so I learn production-ready patterns.
• Clear acceptance criteria for every milestone—performance targets, data quality checks, and documentation—so I can measure my progress objectively.
• A final assessment in which I design, implement, and present a complete pipeline solution, receiving detailed feedback on scalability, fault tolerance, and optimisation choices.
If you have senior-level experience architecting and delivering Python-based data platforms and enjoy mentoring, I’d love to hear how you’d guide me through this journey and what materials or environments you’d suggest we use.
Here’s how I picture the collaboration:
• A structured roadmap that breaks the three domains into progressive weekly objectives, each paired with practical tasks I can build out in my own environment.
• Live, interactive sessions for concept walkthroughs, white-boarding architecture, and code reviews. Recordings or detailed notes after each session are a must so I can revisit the material.
• At least two end-to-end mini-projects: one warehousing/ETL build and one real-time or batch pipeline, both deployable locally or on a cloud sandbox. These should incorporate version control, unit testing, and monitoring so I learn production-ready patterns.
• Clear acceptance criteria for every milestone—performance targets, data quality checks, and documentation—so I can measure my progress objectively.
• A final assessment in which I design, implement, and present a complete pipeline solution, receiving detailed feedback on scalability, fault tolerance, and optimisation choices.
If you have senior-level experience architecting and delivering Python-based data platforms and enjoy mentoring, I’d love to hear how you’d guide me through this journey and what materials or environments you’d suggest we use.