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Angela Ferreira is a senior analytics engineer at Infinite Lambda. An active member of data community, she is passionate about helping others enter the tech industry and progress on their learning and career development journeys.
Angela is vocal about women’s experiences in STEM and in the data industry in particular. She is a member of Infinite Lambda’s DEI Committee, where she helps drive initiatives that aim to create inclusive environments for everyone to thrive in.
In this interview, she shares her insights to help peers find a job in data and build a successful career in an AI-first landscape.
You have been in data for over six years. If you were starting today, what would you do differently?
This is a tough one to start with. Looking back, I think I would simply try to be less hard on myself, but that is something we only get to learn with time.
Early in my career, I used to focus too much on what I didn’t know, and not recognising and developing what I was already good at. I was always chasing my gaps instead of leveraging my strengths.
If I could turn that reflection into practical advice, it would be to ask for feedback from leaders and peers, identify your strengths, and keep improving them even more than your weaknesses. That is how you become truly exceptional in a specific area.
Of course, you should not ignore foundational gaps. Just do not let them define your self-worth or your entire learning journey.
But one thing is for sure: if I were starting out in data today, I would definitely recommend following relevant people on LinkedIn to understand what skills and topics matter most in your field. Joining data communities is also a game changer, as it gives you support, references, and a sense of belonging that makes all the difference when you are just starting your career.
What would you say to data professionals who worry about AI automating parts of their job?
Do not be afraid, be curious instead. The key is to focus on bringing business value and truly understanding the why behind what you do. If you are a data professional who can connect technical work to business impact, you will always be needed.
AI is an incredibly powerful tool, but that is exactly what it is: a tool. It can automate repetitive tasks, reduce manual work, and free up time for us to focus on more strategic, creative, and high-impact projects.
I believe the future of tech belongs to professionals who can bridge gaps between people and tools, decisions and data, business and tech. And that is exactly why analytics engineers should not fear AI: we thrive at that intersection and connect seemingly opposing sides to drive real value. AI only helps us do that even better.
How does the community help you stay up to date?
In the data industry, community is key to almost everything. Since I started working with data, I have been part of several communities, and it is amazing to see how people across different companies and contexts face — and solve! — similar challenges. It really broadens your perspective and shows you that there are many different solutions to the same problem.
I started by following professionals I admired, and their content became a great source of learning. Newsletters also helped me stay on top of trends and new ideas.
Last but not least, the community I have at Infinite Lambda is really supportive. As a data consultancy, we are constantly exchanging experiences; there is always someone who has worked on a similar project or is sharing valuable insights.
That collective knowledge makes problem-solving faster and creates a strong sense of support and collaboration.
What principles guide your daily work?
Everything I do, I never lose sight of the following:
- Business impact: I want my work to drive real results for the business. I try to avoid building technically beautiful solutions just for the sake of technology if they do not create tangible value for anyone;
- Scalability: I aim to design scalable, well-governed solutions that can keep running smoothly even without my direct involvement;
- Automation: Always looking for ways to reduce manual work, lately I have been exploring how AI can help with that. I use it strategically to work faster and more effectively.
Beyond that, I stay curious about new practices in the data world and how I can bring more data governance principles into my analytics engineering work.
Are there any particular tools you are learning right now?
I am a huge dbt and Snowflake fan. They are at the core of my daily work, and I love exploring how their newest features can help me and my team scale our impact. There is always something new to learn about both.
How has the experience of seeking a job in data changed over the past few years?
With the rise of AI, I do feel everything from learning to applying for jobs has become faster and more accessible. But I still believe in adding a personal touch to everything career-related. Time and focus have influenced my approach to job seeking more than any big tech movement.
Certainly, one of the most interesting shifts in the data world has been how roles evolve over time. From data analyst and BI engineer to analytics engineer, data engineer, data scientist, and now all the way to ML engineer and AI engineer, we have come a long way. I am positive this will keep changing.
What trends do you think will shape the next few years of job seeking in data?
Everything related to AI and data will keep growing for quite some time, only in a new form. Technical skills will always matter, but professionals will also need to develop strong business understanding and soft skills. Coding and building pipelines will no longer be enough; the real value will come from connecting technology to business impact.
While AI is doing incredible things, it is no good without data, which is why I can see analytics engineering becoming even more relevant that it already is. As it sits at the intersection of data engineering and data analysis, its versatility will be essential, with roles becoming broader and more hybrid, placing an even stronger focus on data quality.
I also believe data governance will play a huge role in achieving AI success, even though many people are still scared of it.
But I do have one concern and that is for the people in the early stages of their careers because AI can make them more vulnerable, limiting their exposure to hands-on experience and business context.
Companies should keep in mind that while this might work in the short term, they still need to make sure juniors have a way in and a path to grow into the next generation of seasoned data professionals.
What should professionals at different stages focus on to stay relevant and find opportunities?
In my early years, I was completely concentrated on building technical skills. I studied hard, closed knowledge gaps, and prepared myself to reach a senior role. I still have a lot to learn on the technical side, but over time, my focus expanded beyond that.
A few years into my career journey, I decided I wanted to find an international role. That shift changed everything for me. I started researching which tools and skills were in high demand, how to make my CV and LinkedIn stand out, and how to build meaningful connections in the global data community.
How did you join Infinite Lambda?
I first came across Infinite Lambda on LinkedIn. I did not know much about the company at the time, but when I read the job description, it sounded like a great fit.
As I mentioned before, I believe that job interviews should go both ways – the company evaluates you, but you should also evaluate the company. Everyone has different priorities, and for me, the key ones are flexibility, work–life balance, career growth, opportunities to study, and good compensation. From my very first interview, I could tell Infinite Lambda offered all of these.
One thing that stood out was the emphasis on respecting local work hours and holidays. Even while working in an international environment, I still get to enjoy time with my family and friends here in Brazil, which is really important to me.
Another factor was the tech stack. I want to go deeper into the modern data tools, and Infinite Lambda not only uses them extensively, but also supports learning and certification goals.
What were interviewing and onboarding like?
The interview process itself was one of the best I have had. Everyone was kind and genuinely wanted me to feel comfortable, so I could show my best. My technical interview was with two very senior professionals (one of whom is now my line manager) and it was exactly what I was looking for: a chance to work and learn from people with deep experience.
When I received the offer, I did not hesitate. I was a bit nervous about being the first Brazilian at the company, but I trusted my gut, and it turned out to be one of the best decisions I have made.
And it only keeps getting better. The culture, the people, and the everyday collaborations proved to be exactly what I hoped for.
What is it like working in a multicultural environment?
Before joining Infinite Lambda, I had worked at a Brazilian company where I was very close to my colleagues, many of them had become my friends, so I was honestly afraid of losing that connection.
But from the moment I joined Infinite Lambda, I felt I was in the right place.
In my very first month, I worked with people from Vietnam, Hungary, Slovakia, Bulgaria, the UK, Argentina, and the US. Quite a mix! 😄 Sure, time zone management can be a challenge sometimes, but we make it work, and I feel genuinely respected by everyone.
What I love the most is how much we can learn from each other. The key to bringing together people from so many backgrounds is not in comparing cultures but in respecting and understanding them.
We do not have a ‘default’ culture here, and that is the beauty of it. We have different accents, time zones, celebrations, and holidays, and see how powerful diversity is.
Personally, I have always been passionate about cultures, cooking, and travelling, so I love chatting with colleagues about their daily lives, and especially food! I would often ask, “What do you usually eat in your everyday meals?” and end up learning something new from someone I would have likely never met otherwise.
Nine months later, I can honestly say I have built strong relationships across the company. My colleagues and I do not just talk about work but also share real connections. This is what makes me feel like I belong here.
This is Part 2 of Angela's interview. In Part 1, she opens up about her own professional journey in data and technology.
Connect with Angela on LinkedIn.
