End-of-Year Report on a 12-Year Data Journey

Introduction: Beyond Numbers

In my previous position, I created end-of-year reports for my business colleagues and CEOs.

Nothing special, you would say. Standard reports with a bunch of numbers per different business areas—from general business controlling, marketing to supply chain management and finance.

True, and I was aware these reports were part of my tasks and would not draw much attention from every colleague in the company.

So, I gave it some thought on how to make the reports more "attention-getters". The answer was simple—make them sound cool.

In other words, I decided to spice up the report names, as spicing up the numbers was not an option.

The difference between any other standard end-of-year report and "mine" was that I named mine after the trending buzzwords this year.

With this said, the 2020 report was named "The Notorious 2020". The 2021 report was named "The Vax 2021", and the 2022 report was "The ChatGPT 2022".

You already have a clear idea of why I decided on these names.

From the world-turbulent year 2020, when the world was affected by the coronavirus, to the vaccination creation in 2021, and to one of the greatest developments in the data world in 2022—the generative AI chatbot launch.

To move on from the end-of-year business reports, I (finally) decided to create a personal end-of-year report on my career progress.

Why, you ask?

The first reason is that 2023 was the year that marked 12 years of my career, and it was—let’s put it like this—transformational. In the sense that I again changed the country, the job, and the living environment at once.

The second reason is that 2023 was one of the biggest years of new technological development since I started my career, and the knowledge barrier for entering the data area has increased.

With all the new developments and sudden demands on generative AI skills in the data field, I think it’s one of the most challenging times to start a career.

To confirm this statement, I will just add that even a pioneer of machine learning, Andrew Ng, recently wrote a memo on how to build a career in AI [1].

It’s little to mention how this memo is truly inspirational.

Not only does it encourage people to enter the field of AI, but it also gives advice on which skills are required and how to overcome your internal "human" struggles when searching for a job or switching career paths.

Although I can’t fully understand how challenging it is to start a data career nowadays, I was able to "find myself" in most of the challenges Andrew described in the memo.

In other words, I know how tricky it is to start building a data career if you didn’t explicitly study this area. The feeling of not knowing where to start, what to learn first, or how to develop is well-known to me.

This is exactly the second main reason I finally decided to write this blog post and sum up the "insights" I gathered in the past 12 years of my career.

With this post, my goal is to help someone "out there" with their data career struggles and make new joiners feel encouraged to enter this area. I aim to humanize this process by sharing my stories and a few lessons learned on the path of building a data career.

And now the same old question has arisen: How to name the report to sound cool?

Again, having in mind that 2023 is the year that marked 12 years of my career, I only thought the appropriate name for the report would be "The Cosmic 2023".

So, let me share with you the journey that shaped me as a data professional and made me decide to build, and stick to, a career in the most amazing field ever—the data field.

The Cosmic 2023: The 3 Stories with Input-Output Flow

As the title of the section states, I will share with you three stories that mark the main stages of my professional data journey.

Story one, named "Per Aspera ad Astra", is a story of how I started a data journey. And how you probably shouldn’t. 😉

Story two, named "Terra Incognita", is a story of how I managed to land my data industry job(s) in a new country.

Story three, named "Semper Crescens", is a story of the next career level and adopting a Growth Mindset in the data field.

Each story has an input-output flow:

• The story input part represents a story background.
• The story output part represents a story outcome.

In the section ending, I will share the lessons learned to sum up the most important insights from the experience.

Story #1: Per Aspera ad Astra 🌟

A story about how (not ;)) to start a data journey without a computer science background.

➡️ Story input

Having studied mechanical engineering (ME), I always lacked passion for most of the ME core courses. By this, I mean the "special chapters" of the courses like Elements of Construction, Mechanics of Fluids, Thermodynamics, etc.

I was just not 100% into the course material, and the best part of every exam for me was when I had to resolve mathematical equations and derive the calculations. This was "green flag #1" I should orient my career in the analytical area.

The "green flags #2 and #3" happened during my master’s studies. I remember it being a hot summer day, and my classmates and I were sitting in the small lab room and attending the course named Information Systems. And there it was—the course where I learned the basics of how to do relational database architecture design and how to create CRUD applications and SQL reports from them.

Later on, I took another course named Information Management, where I was able to learn about advanced analysis using only SQL. I loved it instantly, and I knew back then this was exactly the area for me.

You would probably expect this to be enough to automatically start working in the data field. However, this was far from my path.

After finishing my studies in 2011, I got an internship at a manufacturing company, where I worked on the project management side. A bit of the reporting was present in my daily tasks, but this was just not "it". I wanted to work with databases.

After almost 1 year, I found an "exit door" in the unstable work market, and I got the position of Research and Teaching Assistant at my alma mater. Accepting the role with a high dose of romanticism, I was finally hoping for my analytical journey to start.

I was, to simply put it – wrong. 🙂

One thing I didn’t think about before was that when you work in academia, no dataset is waiting for you. In a lot of cases, there is not even a project or a company partner waiting for you. You need to find both on your own.

To get to this point, i.e., to find my company partner who would give me a dataset, it took me another 3 years. I won’t go into details about the problems and obstacles along the way, as this is a topic for another blog post. 🙂 But I can tell you it was an iterative process of networking and relying on good people’s beliefs in me.

After this longer process, I just remember the feeling of being proud and having immense motivation to work with "my precious" data.

⬅️ Story output

I finally got my datasets.

And, as soon as I thought the hardest part was behind me, the difficulties only began.

Questions were piling up: What to do now with this data? How do you develop something new to have a scientific contribution?

I had no clue. No clue where to start or how to finish the analytical part of my thesis.

Then somehow, in the process of reading scientific publications, I stumbled on the area called "data mining".

And there it was again—something I had been waiting for. The analytical techniques I can use to develop my models from the obtained datasets.

If only it was as easy as that.

The new struggles began once I delved into the world of ETL processing to prepare the data for the modelling stage. I had messy, missing, and imbalanced datasets without proper joins that had to be joined together. This was the time when I was regularly staying awake and working until 2-3 AM with my business colleagues (note: they were Aircraft Engineers working in the Maintenance Control Center with a 24/7 working schedule) to understand different data sources in the core.

Once this battle was won and input datasets were prepared, the next one was learning about machine learning modelling. From feature selection and dimensionality reduction to selecting the proper machine learning algorithm and understanding the math behind it. On top of that, I was collecting knowledge of statistical analysis and how to evaluate, compare, tune, and present the model outcomes.

This all took another 3 years.

Finally, putting it all together, it took me 6 years to be able to say that I understand how to create value from data.

In other words, this time I finally got a degree that had "something to do with data". 🙂

Story #2: Terra Incognita 🗺 ️

A story about how to land industry job(s) (in a foreign country).

➡️ Story input

Following my "big-life victory", i.e., getting a new degree, I started working on my next career move—finding an industry job.

I created a completely new CV, as the old one was no longer a trend in the market. I created profiles on all job and recruiting portals in the country. And finally, I started applying for the few available data-alike positions listed and sending open applications.

I say "a few available positions", as the market in 2017 in a country of 4 million people was not in high demand for these roles back then.

If I recall properly, all together in Q4–2017, there were 4–5 data positions open to which I applied. From these 4–5 vacancies and several open applications, I managed to land 2 interviews.

After passing the initial assessment tests (technical, intelligence, and/or organisational), the next stage was an interview with the hiring leads.

Again, I won’t go into the details here, but I will just add how I wish I could forget some of the questions I got in these interviews. One direct question was "What do you even know?" after I elaborated on my thesis work.

You can imagine my level of confusion about this and similar questions. I felt this was not okay, and things should look different. I should be treated differently.

I took matters into my own hands and made a plan to search for a job where I could get an equal chance, where things were different, and where I could get more life opportunities.

To cut to the chase: I __ got different after a while. I got a job in a different industry in a different country, with all the different challenges in the package. 🙂

It was not the smoothest road to get to this point, and the following statistics will give you a better idea of the process:

• Time duration: it took me 14 weeks;
• Number of applications: 60 job applications;
• Number of landed interviews: 3 interviews with several rounds (2–3);
• Number of offers: 1 job offer.

As you can see from the above-listed numbers, I was in a hurry. I spent most of my free time looking at new job openings on a specific job board and writing cover letters.

However, every minute spent on this was worth my time. I got a job.

⬅️ Story output

I finally got it—the job as an IT consultant. Someone thought I had what it takes for this role, regardless of the missing language skills, not knowing how to work with specific tools, and bureaucratic obstacles (working permit). 🙂

Accepting this offer was a no-brainer for me, and I knew it was a good choice. The first reason for this was that, as a consultant, I was again working on projects. So, something similar to what I had in my previous role. The second reason was the knowledge. This job gave me the freedom to collect and build knowledge in new data areas.

This was the time when I got acquainted with data warehouse architecture design concepts and cloud platforms. Both areas were completely new to me and extremely interesting.

And then one day I got a call from an unknown number. It was a call from the recruiter, explaining something about the new team starting a new data project and having my CV from almost a year ago on his stack.

At first, I didn’t get what this call was about, but I agreed to have a follow-up call. What I can still recall from the second call is one piece of information: "You will work with billions of records." My eyes sparkled. Never before have I worked with Big Data, and this opportunity sounded great. So, I decided to take it and start a new role—as a Data Engineer on a Big Data migration project.

The job was fulfilling—learning intensively about coding and co-developing near-real-time data ingestion pipelines, as well as co-developing analytical customer-oriented models and insights. Again, I was collecting new data engineering and data science knowledge by working on use cases I had never worked on before.

This process was constant…until one day, the company went bankrupt.

Well, this was not in my plans by far. 🙂 I was suddenly jobless and started sending out my CV in the pandemic pre-summer (i.e., dead) job season.

Luckily enough, by this time I already had a small network, and one of my former colleagues recommended me to his company. His company was migrating their analytical dataset to the cloud platform and had no one to work full-time on this topic. The stars aligned—I was jobless, and they were in a search. Win-win situation. So, again, I started my new role—as a Data Analyst.

This position has shaped me in different directions. The ones I never expected.

The reasons are numerous: from being a business analyst to being a data generalist (analyst, engineer, and scientist in one) and working on the first line between IT and business colleagues, from learning almost every aspect of the business through data, and from being a "one-woman show" to building a data team.

To sum it up, it took me another 3 years, or altogether 9 years, to progress to the next career level.

Story #3: Semper Crescens 🧠

A story of the new role and working towards adopting a growth mindset.

➡️ Story input

The next career level—I got an offer to build a data team and become a Data Lead. I mean, I already have 6 years of experience as a mentor and coaching students, so this should be easy, right?

Except it wasn’t.

But let’s rewind a bit on how I arrived at this point. I still think to this day the most influential factor was "being in the right place at the right time" .

Meaning: I was the first to work full-time on a migration project; I had experience in different data fields; the business requirements on the data side were exploding; and I brought ideas on how to create a long-term data roadmap. Again, the stars aligned, and apparently, it was a logical choice to be "the one" who would form and lead a data team.

A leap of faith was given to me. So, I grabbed it and started a new role.

It would be an understatement to say how lost I was in this position at the beginning. It was again like building a new career and not knowing what to do first.

Initially, I was not able to let go of the technical work, and I was clinging to it. I mean, I did all the development from scratch, and now someone wants to take it over? Although I was not able to manage the development requirements by myself anymore, it was hard to let go of hands-on work.

Then, I realised I needed to do "human-oriented" tasks too. Motivate colleagues, conduct 1-on-1 talks, provide guidance wherever possible, create team vision and objectives, lead hiring, and all together, create a pleasant working environment.

As a bonus, there were other tasks—the "management" tasks—doing team budgeting, controlling, collecting, and organising work per data role, and presenting the team.

These were __ challenging tasks for me, and I needed help.

Luckily, I had it. Not only did I get support from my supervisors, but I also got support from my peers. In addition, I even got support from former supervisors and their peers.

However, this was not enough for me. The real change happened, and the role became easier when I started reading books on psychology and organisation, following people who were sharing their stories on leadership, attending workshops, and taking coaching.

After this, I realised everyone can grow in unknown dimensions faster and more successfully with proper guidance. And these were my first steps to acquiring what I later discovered is called a "growth mindset".

⬅️ Story output

Now, I am not saying I possess a growth mindset in every life situation. In the end, I am only a human, and my fixed mindset is present in me. However, I will say I am dedicated to getting one.

Like everything else in life, this takes constant work and discipline. It takes being able to reflect and take several steps back when needed. It takes not caring if you turn out looking ignorant sometimes. And it takes focusing on your growth through learning.

Finally, you realize that it’s not about chasing roles and the hierarchical ladder; it’s about knowledge.

After the 12-year journey and being currently in the second Data Lead role, the important thing for me is that I further acquire knowledge inside and outside of the data field. Especially when it comes to generative AI development, as it will impact the entire way of working in the data industry.

Except for this, I believe I am finally able to empower and support others on a similar path by sharing my experiences. All to create more interest in the data field and attract new talents.

Lessons learned 🧐

I will try to keep it short here and list the most important lessons learned on the 12-year-long path described before. More technical details will follow in another blog. 😉

Data Career Essentials

• Build the foundational knowledge. Understand the importance of foundational knowledge in the data field. In other words, gain logic by learning math and statistics first, algorithms, or understanding data structures, architectures, and coding principles. It’s a lot, of course, but knowing general concepts will make your hands-on work easier later on.
• Deliver quality work (whenever possible). Prioritise quality over quantity in your work, and adopt a methodological way of working for clarity and better performance.
• Avoid excuses (whenever possible). It’s always easy to justify yourself and find excuses for—well, everything. However, taking responsibility and owning your mistakes will make you stand out from the crowd.
• Create your opinions (whenever possible). Develop critical thinking skills by evaluating and questioning existing findings and conclusions.
• Always keep learning. Recognise the value of self-paced learning and continuous education by using online e-learning platforms. Learn about business, psychology, and other sciences to complement your technical knowledge.
• Do personal retrospectives. Keep track of your failures to measure your progress later on.

Data Career Personal Insights

• Search for so long until you get a "yes". Train yourself to be persistent in challenging career situations, and don’t take "noes" personally.
• Learn that "no, thank you" is a full sentence. Focus on the goals that matter to you, and respect others along the way.
• Someone’s ceiling is your floor. Probably the best advice I ever got was not to limit my ambitions on account of the viewpoints of others.
• Credits where credits are due. Understand the importance of giving credit and the value of sharing knowledge.
• Rely on 2F. Seek support from friends and family during tough times.
• It’s you against you. Your career is not a competition, and no one has the same starting point in life. Everyone is struggling on their own paths, and it makes no sense to compare yourself to others. It is only you against you.

Conclusion: "There is no cure for curiosity"

By sharing my stories in this blog, I wanted to normalise the struggles on the path of building your data career. I aimed to give you examples of how every obstacle can be resolved if you persist in finding solutions and working towards your goals.

With this, I wanted to motivate the people who didn’t study this field and the ones who are thinking of switching careers to join the data field.

It’s probably not going to be a smooth journey, but have confidence that every second will be worthwhile a few years from now—maybe 3, 6, 9, or the cosmic 12. 😉

Lastly, I will end this blog with a saying from Dorothy Parker:

The cure for boredom is curiosity; there is no cure for curiosity.

So, be curious and join the most amazing field ever – the data field. 🙂

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Thank you for reading my post. Stay connected for more stories on Medium and LinkedIn.

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Acknowledging the pillars of support

As this is an end-of-year "report", I need to express my gratitude to everyone who shared and is still sharing my journey.

• Family & Friends. On the path of pivoting to and sticking to a data career, I got immense support from family and friends. You cried and laughed together with me through challenging times. Thank you for sticking with me.
• Mentors & Colleagues. To my former supervisors and their peers, whom I considered mentors and whom I can still call today for a piece of advice. The same is true for my colleagues. Thank you for everything I have learned from you.
• Community. On Medium and LinkedIn, I crossed paths with people who inspired, applauded, and shared my stories (TDS). This sometimes puts an amazing light on my days. Thank you for sharing your kindness.

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Knowledge references

[1] DeepLearning.AI resource, "How to Build Your Career in AI" by Andrew Ng, accessed October 13th 2023, https://info.deeplearning.ai/how-to-build-a-career-in-ai-book