Join Lucie, Vista Engineering Domain Lead for Business Performance Analytics (BPA) at Vista Order and Sales Division, as she takes us through her exemplary journey in the field of data engineering and how at Vista, she got an opportunity to explore and grow her career.
Data Engineering has emerged as a top career choice recently. And it does not explicitly need a degree in data engineering but an analytical mind to become a data engineer. A viable approach is to develop a learning mindset and start with some foundational languages. You can carve your path. Lucie Tohme, Engineering Lead at Vista, is a bright example. She had a PhD in Physics and limited technical expertise. The fantastic world of data attracted her, and not only did she switch her career path, but she also managed to excel in the field of Data Engineering. Now Lucie is an integral part of Vista Order and Sales Division, leading the BPA domain.
Here’s all about her inspiring journey, where she revealed everything that motivated her and helped her build a career in this field.
Which team are you part of, and what is your domain?
I recently moved to be the Engineering Lead of the Domain BPA. This domain provides key reporting products to understand Business Performance to stakeholders across the company, including executives, Finance, North America, and EU Regions, as well as partnering with Manufacturing to provide detailed reports and forecasts for our manufacturing and supply chain operations.
Just before this change, I was part of one of the Data and Analytics domains, Customer & Business Performance Monitoring (CBPM), primarily focused on the customer journey. We track customer information and align this data to make their journey better. We achieve this through continuous experimentation using efficient tools and finding the best-suited actions to improve customer relations. We also provide a suite of tools to help other Data Product teams continuously improve their results through better identification of customer value.
What triggered you to take Data Engineering as your career?
The trigger was my training in the concerned field. I realized how much could be done with data! Even raw data can be improved and used for analysis, helping with better decision-making. The limitless possibilities that data offered inspired me to learn more about it. I also wanted to develop the required skills to work for this purpose – how data could enable better outcomes. Besides, my exposure to sophisticated tools and technologies also drove my curiosity to explore more.
What skills have you acquired that helped you excel in a Data Engineering role?
I have been passionate about Physics since I was a child. Physics allowed me to understand the mechanics behind the working of different instruments, how individual components worked, and what forces combined them. My Ph.D. was focused on creating a tool that facilitates communication at high frequencies, say 300-400 GHz.
The takeaway from this experience was that it streamlined my curiosity. Besides, I was working with tools like MatLab and LabView that helped me with automation and consequently developed my mindset for research. These tools are both software tools commonly used in data engineering, as Matlab is more focused on mathematical computations and data analysis, while LabView is more focused on instrument control and data acquisition.
What else did you learn before you applied for the job?
Predominantly, I self-trained and learned languages like R and SQL, which are primarily used for statistical analysis and managing data in relational databases.
I joined as a Junior Data Engineer in a team called Analytics Engineering. And the best thing about Vista is that it isn’t looking for only super-experienced people with impressive technical skills, but it is also ready to absorb people willing to learn. I was a complete fresher in the technological space and did not have the necessary skill sets. But what drove the team to accept me was that I was curious and willing to learn. So, Vista does not discriminate and offers you the opportunity to learn, explore and grow, no matter your background. I learned all the basics of Data Engineering with Analytics Engineering. I can’t thank this team enough for trusting me and for spotting the potential I had as a junior engineer.
What problems did you start tackling at the beginning of your career, and how did you grow?
Being a part of the Data Tribe and the Analytics Engineering team, one of the first things I started working on at Vista was improving the code quality and testing. It was challenging, as we had a code base created by different team members with diverse technical expertise like Data Analysts, Data Engineers, and Software Engineers. This codebase was complex to maintain, and as we were the team that linked Data Tribe with Analysts and Marketing, we had to take care of two things primarily:
- Focus on the outcome and debugging part. We had to understand the business needs, design the code, and define the way of coding so everyone could easily use, understand and maintain it.
- Creating alerting and quality checks. Since we used to work on the SQL servers previously, we had to build the entire framework for alerting. We created store procedures and macros that allowed us to have quality checks and monitoring from scratch. We also used SQL jobs to send alerting emails automatically to the right people depending on the error or the alert type. To be precise, we had to automate the entire process and work towards improving different aspects to make things better. I got an opportunity to learn new tools like APS, Tableau, and Jenkins. Besides, I needed to upskill and develop a good Python base. Switching from SQL to Python was a significant milestone for me. Python is a language that opens so many doors. You can do so much using Python!
Any significant achievements in learning this language and implementing it?
The use case I worked on initially that allowed me to learn Python was automating the creation of weekly report PDFs from spreadsheets. This helped me learn Python and saved hours of manual work every week so that the person could focus more on value-driving tasks for the company. Learning Python through this use case surprised me as I discovered that there was much more to do than I expected. After that, the domain switched to entirely new platforms and tools as we moved from SQL to Snowflake and Tableau to Looker. We had to build everything from scratch in Snowflake and Looker. All the codebase and tools had to be revisited for these new tech stack. As we moved to these new technologies, it helped me take on more responsibilities, such as building the knowledge base for Looker and sharing it with the DnA domain and using new techs like dbt (data built tool) and Airflow. This technology change helped me accumulate more knowledge and experience in a leadership role.
You eventually got promoted to Lead and Manager, DE. How did this change come?
It was a complex change in the beginning. I feel that as an individual contributor, it gets easier to validate the outcome of your work. But, as a manager, I had to learn more about teamwork to understand how we could collaborate and grow together. And also how to set my team up for success and deliver through others, which is a hard balance and learning for someone who has been a strong individual contributor.
I needed to work on my soft skills and develop my technical skills in parallel. It was more about the team – how I could influence them and build their trust. It was overwhelming at the beginning, but you do grow with time. It took a year for me to understand what the role was and what it demanded. It’s more like an investment in the future. I also got a lot of support from my manager, colleagues, and Vista with training and sharing advice and experiences.
According to you, what are the essential skills and strengths of a proficient data engineer?
Technical skills are not the primary concern. They can be developed with time. If you have the motivation, you can learn things and improve. You must be keen on learning, adhere to the team’s way of working, and follow the established Engineering Excellence Principles. However, the most important skills, in my opinion, are curiosity and adaptability. You will likely succeed sooner if you can become a cohesive part of the environment and fit in better.
What is the biggest challenge that you face regularly?
I feel that the biggest challenge is to get out of your comfort zone and keep on improving. You must remain constantly updated with the new technologies, adapt them to your work processes, and influence your team to try them. The focus is to identify improvement areas and work towards making things better. I constantly think about how to help people grow so that they do not remain stuck in their current position.
What’s the next step?
My next objective revolves around the new organization at Vista. I am eager to explore ways in which I can effectively support my colleagues in the BPA domain, including the Data Product Manager and the Plant Analyst Manager. My goal is to identify how I can assist each team member within the domain to grow professionally and contribute to Vista’s overall success in achieving our objectives. I also want to encourage collaboration with the leaders of the Order and Sales Division, allowing us to address shared topics and enhance cooperation.
As I said before, adopting a learning mindset is crucial to me. Today, I recognize the importance of acquiring the necessary skills to provide optimal support in my new role within the Manufacturing and Plants’ teams. This area is entirely new to me, which generates a great sense of excitement. This enthusiasm grows my desire to explore new territory within Vista and even Cimpress.
What advice would you give to someone starting a career in data engineering?
- Be curious and have a learning mindset to read and learn about new tools that top companies use.
- Develop a solid technical base. Beware of your environment, and keep on thinking about what could be the best next step for you to grow. Have an idea for the future.
- It’s all about taking the first step. Do not be hesitant when something is good for you.
- Experimenting is fine. Try things out! It may all work out for you, and eventually, you will find your way.
Interested in Data Engineering at Vista? Follow us on LinkedIn to stay updated, learn more about Vista’s journey on our blog, and check out open positions here.