Seeking Free Review — Global Talent Visa Application (Digital Technology): Nigeria-based Senior Data Engineer

Stage 1
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Hello everyone,

I am preparing my UK Global Talent Visa application under the Exceptional Promise route in Digital Technology. My profile is focused on Analytics Engineering, Data Platforms, Open Source Software, Technical Community Building, and Developer Education.

I would appreciate honest feedback on whether my evidence mapping is strong enough and whether any evidence appears weak, duplicated, or better suited for another criterion.

Proposed Evidence Structure

Mandatory Criterion – Recognition as an Emerging Leader in Digital Technology

MC01 – Open Source Contributions to the Data Ecosystem

Evidence includes multiple open-source projects developed outside employment, including multiple open-source projects, a metadata connector, GitHub repositories, contribution history, repository analytics, adoption metrics, downloads, stars, forks, and community usage evidence.

MC02 – International Recognition Through Conference Speaking

Evidence includes speaking engagements at major data conferences and community events, including Airflow, Airbyte, DevFest Lagos, Global Data AI Conference, Write the Docs Nairobi, several data community events in Nigeria, and virtual speaking engagements reaching audiences across Africa. Evidence includes speaker profiles, conference agendas, event pages, presentation materials, recordings, and event photographs.

MC03 – Community Leadership Through Behind The Data

Evidence includes founding and operating a data-focused community platform and newsletter featuring professionals across the global data ecosystem. Current reach includes 3,000+ subscribers across 65 countries and 26 Nigerian states. Evidence includes subscriber analytics, growth metrics, engagement data, featured interviews, community activities, and platform impact.

Optional Criterion 2 – Contributions Beyond Occupation

OC2-A – Open Source Contributions Beyond Employment

Evidence includes voluntary development and maintenance of open-source tools and packages for analytics engineers and data practitioners. Evidence includes repository activity, project documentation, community adoption, user feedback, and public availability of the tools.

OC2-B – Mentorship and Industry Judging Activities

Evidence includes participation as a mentor within data-focused communities and educational initiatives, as well as invitations to serve as a judge for technology and innovation competitions. Evidence includes invitation letters, judging records, programme information, testimonials, and community impact.

OC2-C – Conference and Community Organising

Evidence includes contributions as part of the organising team for the Open Source Analytics Conference and other community initiatives supporting open-source education and professional development. Evidence includes organiser listings, responsibilities, event materials, and community impact.

Optional Criterion 3 – Significant Technical Contributions

OC3-A – Technical Contributions Within Employment

Evidence includes employer letters and supporting documentation demonstrating significant technical contributions in analytics engineering, data platform development, and modern data stack implementations. Evidence focuses on technical ownership, architecture decisions, delivery impact, and measurable outcomes.

OC3-B – Open Source Data Platform for Startups

Evidence includes my Master’s capstone project involving the design and development of an open-source data platform intended to help startups adopt modern data stack practices. Evidence includes architecture documentation, source code, technical design decisions, project scope, and adoption plans.

Additional Supporting Evidence

  • Featured by Tableau (in 2023) as a community contributor and an Airflow champion in 2025.
  • Public profiles and recognitions across data and open-source communities.
  • Recognition received through speaking engagements.
  • Conference publications and speaker listings.
  • Recommendation letters from senior technology leaders and recognised industry professionals.

Questions for the community

  1. Is the Mandatory Criterion strong enough with open source, conference speaking, and community leadership as the primary evidence?

  2. Would you place the newsletter/community initiative under Mandatory Criterion or Optional Criterion 2?

  3. Is OC2 stronger when focused on mentorship, judging, conference organising, and open source contributions outside employment?

  4. Does the Master’s open-source data platform fit better under OC2 or OC3?

  5. Do you see any significant overlap between the open-source evidence used in MC01 and OC2-A?

  6. Are there any pieces of evidence that appear weak, unnecessary, or better suited for another criterion?

I would appreciate any honest feedback before submission.

Thank you.

2

@Ayoade_Adegbite

Are you a Data Engineer or a Software Engineer. Every evidence check ultimately ties back to your skill area and discipline. For context, For speaking evidence (The topic your spoke about and the event’s theme) will be assessed against your discipline. For instance, a “Product Launch International Conference” aligns naturally with for a Product Manager, not a Data Engineer. When an applicant is unclear about their skills and discipline, it weakens the overall application and makes the assessment less favourable.

So, I need to know your discipline, more details about the evidence your are submitting. So, Open source can work for MC, but what specific evidence are you submitting.

Can you be more specific about the evidence you are submitting, your role, and I will be able to give more useful feedback and guidance.

All the best.