Nigeria can leverage AI, data engineering to address unemployment, insecurity —Oyeyemi, data engineer

Dayo-Oyeyemi

Dayo Oyeyemi is a data engineer and machine learner engineer, AWS cloud developer and data scientist. The Osogbo, Osun State-born data professional decided to pursue his passion of leveraging technology to drive innovation beyond the shores of Nigeria and making exploits in the United Kingdom in the digital technology sector. In this interview by NCHETACHI CHUKWUAJAH, he speaks on how Nigeria can leverage such technologies as Artificial Intelligence (AI) and data engineering to address the challenge of unemployment and insecurity in the country.

Tell us about your journey into AI and data engineering field?

In my previous role as a key python developer with Integrated Software Developers Group, I played a pivotal role in delivering innovative software products to small to medium-sized enterprises (SMEs) across Nigeria. My contributions spanned from developing software applications and machine learning systems to designing e-commerce websites. Through dedicated efforts, I not only increased company revenue but also streamlined workflows and enhanced the online presence of various SMEs. Additionally, I leveraged the power of software, data and machine learning models to assist SMEs in price prediction and customer retention, thereby reducing churn rates.

Driven by a relentless pursuit of excellence, I made a decision to further expand my expertise in data and AI applications by pursuing a Master of Science in Artificial Intelligence and Data Science at the University of Hull in the United Kingdom. My dedication and hard work paid off as I graduated with distinction in my research project, culminating in a project where I developed a machine learning system for forest fire detection.

After my graduation, I was offered a position at Giacom as a data engineer, where I am currently tasked with building data platforms on AWS from the ground up. With five years of experience in the field, I specialise in architecting end-to-end data platforms that drive business success through actionable insights and innovation. I have witnessed firsthand the transformative power of data engineering in driving business performance and innovation and I am committed to continuing this journey of excellence at Giacom.

My journey from Osogbo to the United Kingdom and my experiences in both academia and industry has shaped me into the data professional I am today. I am excited to continue pushing the boundaries of what is possible with data and AI, and I look forward to making a meaningful impact in the field for years to come.

What was the motivation to go into AI and data engineering?

My journey into the digital technology field with my expertise in AI and data engineering, and software development was motivated by a desire to leverage data to address real-world challenges. Over the years, I have spearheaded initiatives to architect and optimise complex data platforms capable of integrating data from multiple business acquisitions. These platforms have not only enhanced business performance reporting but have also empowered stakeholders to make data-driven decisions with confidence and agility. In today’s data-driven world, the role of data engineers has never been more crucial. As businesses continue to expand and diversify, the need for robust data platforms capable of handling diverse data sources has become paramount.

I was drawn to AI and data engineering by the transformative potential of data in driving business outcomes. Witnessing the impact of these technologies on industries motivated me to specialise in this field, driven by a desire to push the boundaries of what is possible with data.

How has the journey been so far? Has there been an impact in your field of technology and current company?

The journey has been incredibly rewarding. I have had the privilege of leading groundbreaking projects and making a significant impact on business performance through data-driven decision-making. My expertise has been instrumental in shaping the data strategy of my current company, Giacom, paving the way for future success.

At my current company, Giacom, where I have been a key engineer, my role has been instrumental to achieving strategic goals. By architecting and implementing robust data platforms, I have enabled our organisation to extract valuable insights, drive operational efficiencies and foster a culture of data-driven decision-making.

I have been with Giacom for one year and in this short time, I have spearheaded the development of end-to-end data platforms built on AWS that have revolutionised how we leverage data. By architecting robust data pipelines and implementing innovative analytics solutions, I have empowered our organisation to make informed decisions and drive business growth.

What has been the major challenge and impact since joining Giacom?

Navigating the complexities of integrating data from multiple business acquisitions has been a major challenge. Through innovative technologies and methodologies, I have overcome these challenges and achieved significant impact, setting new standards for data excellence within the organisation.

From your perspective, what should data engineers and businesses expect in 2024?

In 2024, data engineers and businesses that rely on data in making business decisions should invest more resources into learning and investing in cloud technologies like AWS cloud computing, Azure cloud computing, Google cloud computing because those cloud resources offer powerful, efficient and cost-effective way of storing, handling and transforming massive dataset on cloud for analytics business reporting and for building predictive machine learning products.

Data engineers, on the other hand, should invest more resources into learning cloud tools and becoming experts in cloud services. Data engineers can take certifications to introduce them to the basics of cloud computing. For instance, they can take AWS cloud practitioner exam. It introduces a beginner to what cloud is, what the service cloud offers, pricing and all the AWS cloud services functionality that a beginner needs to understand about processing, transforming data from landing layer, staging layer and conform to summary layer, for business use.

In 2024, businesses should be ready to invest in cloud technologies and data engineers should be ready to learn cloud technology to optimise their workflow.

How can businesses and people prepare for these changes and challenges?

The most effective way for businesses and people to prepare to handle massive changes and challenges of pulling complex variety of datasets under the same storage unit is to invest in cloud technologies. Although those technologies are not the perfect solution, they get the job done in most cases. They should just prepare to invest in setting up their data teams on AWS cloud, Azure cloud, Google cloud because it increases the way data are pulled and pushed from various sources. It allows proper maintenance of cloud resources. For instance, lambda function can be built to do data cleaning, deduplication, data schema transformation.

I think in 2024, and to handle variety of datasets coming from complex system and to handle the volume of dataset now, businesses should be prepared to invest in setting up their teams on cloud platforms. With this, the challenges can be managed effectively because I have seen this work firsthand.

Giacom has offered me the opportunity to perform advance data engineering and to support business revenue performance reporting by building highly scalable data pipelines on AWS cloud, I efficiently pull big datasets from various complex business sources under the same cloud platform. So, it is actually the best way to handle data complexity and volume and the changes that we are going to experience in 2024. With this, I can assure that businesses and individuals will thrive if they invest in cloud technologies.

How can AI, data and data engineering be used in addressing issues of insecurity, high unemployment rate and prevalence of kidnapping in Nigeria?

In addressing the pressing issues of insecurity, high unemployment rate and the alarming prevalence of kidnapping in Nigeria, a multifaceted and forward-thinking approach is imperative. Harnessing the untapped potential of Nigeria’s youth population in the fields of AI and data engineering presents a sophisticated and thought-provoking solution that holds promise for transformative change.

By strategically investing in education, training and infrastructure to cultivate a skilled workforce of AI and data engineers, Nigeria can not only address the challenges of unemployment but also foster innovation and economic growth. These highly trained professionals can leverage their expertise to develop advanced data analytics solutions for predictive crime mapping, early warning systems for security threats and optimised resource allocation for law enforcement agencies.

For instance, I built a machine learning system that predicts forest fire occurrence utilising meteorological data point such as temperature, windspeed, rainfall and humidity, which has been adopted massively in providing early fire predictions, thereby reducing loss of human lives and properties and preservation of the ecosystem.

Moreover, collaborative partnerships between government, academia and industry can facilitate the creation of initiatives aimed at tackling insecurity through data-driven approaches. By harnessing the power of big data analytics, machine learning and AI algorithms, stakeholders can gain actionable insights into the root causes of insecurity and devise targeted interventions to address them effectively.

Furthermore, empowering local communities with access to technology and data literacy skills can foster a culture of innovation and entrepreneurship, creating opportunities for socioeconomic development and reducing the vulnerability to criminal activities such as kidnapping.

Leveraging the talents of Nigeria’s youth in AI and data engineering represents a transformative solution that not only addresses immediate security concerns but also lays the foundation for sustainable growth and prosperity. Through visionary leadership, strategic investment and collaborative action, Nigeria can chart a path towards a safer, more prosperous future for all its citizens.

Given Nigeria’s youth population, do you think it can be the hub of AI and data engineers and how can these skills be harnessed to the country’s economic advantage?

Absolutely, Nigeria’s youth population presents a tremendous opportunity to cultivate a thriving hub of AI and data engineers. By investing in education, training and infrastructure, Nigeria can harness these skills to drive innovation, economic growth and societal advancement, positioning itself as a global leader in AI and data engineering. Collaborative partnerships between government, academia and industry will be key to unlocking Nigeria’s potential and harnessing these skills to the country’s economic advantage.

What are your thoughts about remote team and data team setup and challenges they face?

From my own perspective, setting up data team remotely comes with some challenges because we have so many barriers, such as language barriers, we have geographical barriers and disability barriers. For instance an engineer can live in the United Kingdom and work for a company that is based in the United States and perhaps, the team is spread all over Europe, Africa, Asian with conflicting time zones.

The most effective way to have this work in each team is to understand what the team is all about; the team values, the goal and what they are trying to achieve because remote options offer companies a unique opportunity to diverse talents pool. Nowadays, most talented engineers don’t really want to be stuck in traffic for several hours. For instance, if you live in London, or Lagos it can be very difficult due to traffic congestions.

In terms of bridging the gap between geographical differences, you can handle that by setting up teams on Microsoft Teams, having Slack channels and setting up periodical stand up calls, technical catchup to understand what each member of the team is working on, perhaps they are having a blocker which can be quickly resolved via peering up with other team members. So, communication line must always be open. Firsthand, we have seen this happen because during COVID many businesses were shut down, but tech teams were still working because they had the opportunity of getting things done remotely.

Since I started working with Giacom, I have been working remotely and I have met my team once or twice in one year. Day-to-day activities such as meetings, communication are done on Microsoft Teams because the company invested in cloud technologies like Microsoft Teams that allows individuals to communicate effectively, set up meetings, share screens and catch up on any blockers. I think setting up remote teams in this era, post-COVID, is achievable with having the right tool, the right people being in charge and having trust in the employees because if there is no trust between employees and employers, the remote team will not work. Managers need to stop micromanaging and have complete trust that the team will get the job done.

There should be this robust sprint planning session that manages what the team will be working on and set realistic users story point, goals and objective within a reasonable timeframe to achieve the sprint goals. Performance can be quantified and measured. I am a witness to having remote teams set up and achieve the aim of getting things done without delay or having issues with deliverables. For instance, if I have an issue with what I am doing beyond my immediate team, I often schedule meetings with stakeholders, we share screen for knowledge sharing and get it resolved.

Working remotely enhances the way communication occurs. For instance, as a person of colour, sometimes you will be working among people of diverse background and skin colour which might affect how you express yourself due to cultural differences and as a minority.  But working remotely in your own space eliminates such barriers. Working remotely offers opportunity to be creative and it reduces a lot of distractions, commute time. So, instead of spending a lot of time in traffic, you put that time into creative work. It also offers flexibility; you can take breaks, utilise your lunch break to pick up your kids from school, exercise, yoga, and recharge.

For remote teams to work, managers must trust their team members to do what they are meant to do, believe them and empower them with the right tools.

Can you share tips for onboarding for new data engineers joining remote teams and companies?

Onboarding new engineers joining remote teams is not a straightforward process but if there is proper documentation, that is, if the new engineers are able to access documentations about various code base and how different internal tools work and how to set up local development environment to align with the team structure and development workflow, that will reduce blockers on new engineers and allow them to settle in fast. My number one go-to option is to have all your code properly documented and ensure that the way you work is documented as well; like how to create, review and merge Pull Request from a local branch into development environment (dev), User Acceptance Testing environment (uat) and production environment (prod). How to resolve Pull Request conflicts should be properly documented, as well as solutions design architecture should be properly documented.

Also, teams should give new engineers probation period to get things running and not throw them into the deep and allow them figure things out on their own; that will add weight on the team performance. The best thing to do for new engineers is to give them access to properly documented code base and expectations clearly communicated. The way of working should be clearly documented as well so that a new engineer doesn’t spend too much time trying to figure out how internal tools work. Deployment pipeline should be properly documented as well.

Again, all production data should be guarded with the right permissions; not all engineers should be allowed to have freedom to do anything on production data. For a new engineer joining a team, regardless of the experience and expertise, I will advice that they should be on read-only role for probably a week or two because they have a lot to learn within that probation period. When things have been properly documented and they don’t have too much freedom in terms of roles like being on read-only roles to do certain tasks, they will thrive. They will make mistakes and when they do, they learn from it. I will advice that making mistakes should be only on lower environments like dev environment and not in production environments.

What, in your opinion, is the impact of AI on data engineering?

The impact of AI on data engineering is massive. AI is the compound name for several Artificial Intelligence usage such as robotics, machine learning predictive model, computer vision and large language models. AWS cloud now offers this machine learning option as a service under glue, which effectively helps in partner matching processes. It is like a predictive modelling that helps predict based on the input data that is being supplied to the model.

AI is improving how we work as data engineers. It is also improving our workflow because some manual processes can be automated, so instead of spending a lot of time tunning models on jupyter notebooks, you can just utilise machine learning resources on AWS cloud with your input data and matches will be done and you have your outcome which can be scheduled to pick up new training data via workflow orchestration. You can also quantify predictive modelling performance using their accuracy score, confidence score, precision, and other metrics. Lots of parameters are being offered on those models on AWS cloud.

The most important thing that AI is bringing into data engineering is automation; you can automate several manual processes to increase productivity in limited time. It reduces burden on data engineers. So instead of having to do many manual works, you can build certain models to do certain tasks based on your input data and they will come out fine. That is what I have seen as the impact of AI on data engineering because now all you have to do is learn how to use those models, feed them the right dataset, define your scope and you will get the benefit of using AI with data and in the data engineering field.

It improves the workflow of a team, team efficiency and it improves time taken to get things done because as a data engineer, you are tasked with so many responsibilities but if you can delegate some of those tasks to those machine learning models to handle, you can deliver a lot of amazing results in limited time and space.

What future endeavours do you envision in the data engineering field?

In the data engineering field, my future involves continuing to push the boundaries of what is possible with data. I aspire to lead transformative projects, drive innovation and inspire the next generation of data engineers to achieve excellence in their careers across the world.

What will be your advice to people wishing to go the AI and data engineering path?

For people wishing to go into AI and data engineering, one of the most crucial elements you should have in your bucket is grit, have flexibility in learning because you have to learn a lot of programming languages. Don’t just base your languages on I know Python, C-Sharp; you must be open to learning new tools because we are in modern age where we have several languages and tools that get the work done faster and companies are adopting different things.

So, if you want to go into data engineering, I will advice that you do your survey, understand the best programming language that is in the market for data engineers and which is in high demand. For instance, I can say categorically that Python, Pyspark, AWS, SQL are in very high demand, so, if you can learn those skills, the sky will be you limit and being able to gather insights and solutions to problems, then knowing how to use Google.

As an engineer, you are basically a researcher and companies pay you to do what they don’t know how to do. Even when you don’t know how to do it, you have to learn. Most, if not all engineers are self-taught before they become better and learn on-the-job. Be open to learning, be open to criticism and be flexible about the tools you choose to work with. Basically, coding is just the way to get things done and not the end. You can achieve your goals in engineering by learning several tools and be a life-long learner and being with the right support, team and company that fits your culture.