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

Stage 1
1

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.

3

Thank you for the feedback.

My primary discipline is Data Engineer. My work focuses on building data platforms, metadata systems, developer tooling, and open-source solutions for modern data teams.

My speaking engagements have primarily focused on analytics engineering, modern data stack practices, metadata management, open source, and developer tooling. Examples include talks delivered at Airflow, Airbyte, DevFest Lagos, Write the Docs Nairobi, and other data-focused conferences and communities.

4

Your MC structure has overlap problems. Open source contributions under MC01 and open source contributions under OC2-A are pulling from the same pool. The Official Guide says “you cannot use the same piece of evidence for more than one criteria.” If your MC and OC2 evidence both rely on the same GitHub repositories, assessors will flag the duplication. Pick whether open source anchors MC or OC2, not both.

The newsletter/community (Behind The Data) fits better under OC2 than MC. The Official Guide’s MC examples emphasise recognition you’ve received: “nationally or internationally recognised prizes,” “speaking at high-profile events,” “published material in professional publications about the applicant.” A community you founded is activity, not external recognition of your standing. For OC2, it works cleanly as “engagement in a significant activity that contributes to the advancement of the sector.”

The Master’s capstone doesn’t fit OC3. OC3 requires “significant technical, commercial or entrepreneurial contributions… as a founder or employee of a product-led digital technology company.” A capstone project is academic work, not a contribution within employment. The guide even notes for OC4 that “research undertaken as part of an undergraduate or MSc thesis does not qualify.” Move the capstone out of your evidence structure entirely, or pivot it to OC4 if you have peer-reviewed publication or supervisor endorsement meeting the academic criteria.

Your strongest MC pieces are the conference speaking and the Tableau/Airflow recognition. Lead with those and keep open source for OC2 only.

1 Like
5

Thank you @Akash_Joshi for your guidance.

I’ll rearrange and drop the updated structure.

6

I spoke at the Global Data & AI Virtual Tech Conference 2025. Following the event, the conference organizers issued a press release highlighting the conference, including my session, which was syndicated across more than 700 news platforms. Sample here: Your privacy choices

Where can this sit properly

7

What is the stats on your openspurce contributions and how long have uou been contributing.
Fod te soeakinv engagement, what year did you speak? You want to demonstrate a track record acrosss multiple years.

8

My speaking engagement started as far back as 2024. and for the open-source contributions: I started this year.

9

2024 speaking could work if it a high profile, well established industry conference. For the opensource, your recognition matter.

10

Your evidence mapping looks logical and aligns well with the selected criteria. The key will be ensuring each document clearly demonstrates impact and avoids repetition.

For the Mandatory Criterion, open source, international conference speaking, and community leadership can form a strong combination if you can demonstrate external recognition, adoption, and measurable impact rather than just participation.

I would suggest keeping the newsletter/community initiative under the Mandatory Criterion if it is one of your strongest examples of wider recognition and influence. If it is primarily about voluntary contribution, it could fit OC2, but avoid splitting the same evidence across multiple criteria.

Your proposed OC2 structure seems appropriate. Mentorship, judging, conference organising, and voluntary open-source contributions are all good examples of contributions beyond your occupation.

The Master’s open-source platform fits better under OC3 only if you can demonstrate technical innovation and meaningful impact. If it remained an academic project with limited adoption, it may not be as strong as your employment contributions.

There is some overlap between MC01 and OC2-A because both relate to your open-source work and could potentially describe similar activities from different perspectives. However, they should be clearly separated in terms of purpose and evaluation focus. MC01 should emphasise external recognition, adoption, usage metrics, and any third-party validation or evidence that your work has been widely used or acknowledged by others in the industry. In contrast, OC2-A should focus on your voluntary involvement, including your ongoing maintenance responsibilities, community engagement, issue resolution, contributions made outside of paid employment, and your role in supporting and sustaining the ecosystem over time. Try to separate them by using MC01 to demonstrate recognition and adoption of your open-source work, while OC2-A to highlight your voluntary contribution and ongoing maintenance of the ecosystem.

Overall, focus on measurable outcomes, independent recognition, and ensuring every evidence document has a clear purpose without duplicating the same achievements across criteria.