Sometimes we forget the humans behind the tech in our ever busy world. DSF is fortunate enough to know some incredible tech leaders across the world and has the privilege of hearing them present at our events. That being said, our Speaker Spotlight sets the stage to get to know our speakers on a more personal level and connect them with our growing community. Read the mini interview below!
A bit about Ollie
Ollie is a Lead Big Data Engineer at ASOS supporting the AI platform, spending the majority of his day building robust data pipelines that scale. He works heavily with Spark, Python, Scala, Azure and Databricks, and is frequently collaborating with data scientists and machine learning scientists alike. He also leads a team of data engineers operating out of the newly-formed Northern Ireland Tech Hub in Belfast!
How did you start out in your tech career?
I originally wanted to work in research as a neuroscientist!
I graduated with a BSc in Cognitive Neuroscience in 2015 from University of Westminster, however it was during my third year that I became jaded with academia – the ongoing reduction in funding and overly competitive nature of the field ultimately helped me to decide that academia wasn’t for me and started looking at alternative career paths.
Shortly after graduating, through some good timing and a lot of luck, I was approached on LinkedIn by a small business intelligence and data science consultancy called Consolidata. They wanted to showcase “problem-solving” at a data conference they were attending by showing off my skills from my speedcubing hobby (competitive Rubik’s Cube solving.) It turned out the CTO’s nephew was the holder of numerous African records in speedcubing (and also a good friend of mine) so they used him instead.
But I was lucky enough to be invited back for a conversation, which ultimately led to my first role in data as a Data Engineer! I learned the core skills on the job: SQL and SQL Server; R and R Server; Hadoop and Spark; and some basic Azure and Python.
What are the signs of success in your field?
A handful of traits of the more successful data engineers I’ve worked with:
– They write simple, easy to read code. Their solutions aren’t over-engineered, relying on too many unnecessary abstractions. Instead, easy to follow and straightforward to extend when things inevitably get more complicated. Simple is better than complex.
– They’re all-rounders. They know a lot about a little, but also a little about a lot. They often have a couple of specialist areas that they are gurus in, but they also have enough high-level knowledge in enough other related areas to be able to understand the bigger picture.
– They’re great communicators. Their day is spent collaborating with other engineers, with data scientists and with non-technical members of the organisation, so the ability to be able to adapt how they communicate to their intended audience is a must, otherwise requirements can get lost or errors can slip through.
– They’re calm and measured. They don’t rush in to close tickets – instead, their approach to designing pipelines and building them is a calculated one. In doing so, they make fewer mistakes during development and fewer bugs get through. They actually turn tickets around faster as they don’t have to fix their mistakes made as a result of oversight or rushing their work!
What is the best and worst thing about your job role?
Best thing: being in that sweet spot between a manager and a pure implementor where I can still write code and build useful software, but also where I can sacrifice some of my time to enable my team to get on with what they’re good at. Devs usually just want to build cool stuff!
Worst thing: the time management and context switching!
What can you advise someone just starting out to be successful?
Get comfortable being uncomfortable.
The start of a career in data engineering requires you to learn about quite a lot of abstract concepts, especially for the completely uninitiated. Languages, frameworks, cloud computing, design, scale, defensive programming, agile working, documentation – it can be overwhelming! And it can be quite mentally draining trying to keep up with all of these ideas whilst also learning about the specifics of the industry or organisation that you work in.
But my advice is to not take it personally. It will eventually click. If you keep putting yourself in new situations that force you to learn new languages or tools then you’ll realise that all of these concepts are interlinked and the knowledge is transferable.
It will make you a better engineer once the dust settles!
How do you switch off?
I wish I knew how! I love long-distance running, especially the marathon; I enjoy playing chess (somewhat recklessly); trying new pubs and restaurants where I live in Essex; and long dog walks with my fiancé and my black labrador Elton.
What advice would you give your younger self?
Don’t give into imposter syndrome! I came into a data job from a non-technical background and so comparing myself to my specialist peers and feeling a bit out of place was inevitable. Some days it drives you to learn more, other days it just gets in your way.
Until I realised that some of my most successful peers came from a diverse background. I’ve known people to transition successfully to data from non-STEM backgrounds like drama and history, and I’ve known people with computer science degrees to not put the effort in. If you’re motivated to learn and solve hard problems then you will do well.
What is next for you?
My focus for the next few years is to keep learning, building useful software and data products and to hone my leadership skills! Beyond that, I would love to keep my options to become either a principal engineer or a data engineering manager, or even to cross-train as a data scientist or machine learning engineer.
If you could do anything now, what would you do? Why?
To own a business! Maybe a data engineering consultancy, or something completely different like running a pub.
What are your top 5 predictions in tech for the next 5 years?
I’m terrible at predicting anything successfully, but I’ll give it a shot:
1. NLP technologies like GPT-3 and ChatGPT will improve exponentially with advancements in GPUs and TPUs, with more data and more experience. They won’t replace engineers or scientists, but they will complement our day-to-day work and boost our overall productivity.
2. AI systems like Dall-E (text-to-image) and Make-A-Video (text-to-video) will also improve exponentially over the next 5 years, which will present challenges (for example, producing extremely convincing deepfakes to spread misinformation.) But hopefully the positive use cases for the tech will outweigh the obstacles!
3. Real use cases will emerge over hype in the machine learning space as it continues to mature. Currently, most ML prototypes fail before they reach production, only 53% make it there. But this number will increase massively over the next 5 years.
4. Use of cloud platforms like AWS, Azure and GCP will continue to grow, and the majority of organisations will run their ETL/ELT pipelines in the cloud. Roles like “Azure Data Engineer” will become more relevant across multiple industries as each cloud provider’s offering becomes more mature, more advanced and more cost-effective compared to on-premise ETL pipelines. [Reference]
5. SQL will still be going strong!
Watch Oliver’s session at the Data Science Festival here.
Thank you to all our wonderful speakers for taking part in our Speaker Spotlight!
Want to become a DSF Speaker? Apply here!