In the Start Up series, we interview influential figures of data science to discover how they first broke into the industry.
Tanmaiyii Rao is Sales Engineer at Snowflake, where she specialises in enabling customers to leverage their data and realise value. Tanmaiyii’s prior roles include Customer Engineer Specialist at Google and Consultant at KPMG UK.
We asked Tanmaiyii to tell us about her journey into data science, her key career decisions, and the challenges she overcame. She reflects on the changing face of data science and the obstacles that remain for women and women of colour in the industry:
From Invisible Struggles to Empowering Discoveries: My Journey with ADHD
My path into tech was unconventional. I originally pursued a Bachelor’s degree in Accounting, Finance & Economics, with the intention of obtaining a Masters in Finance and landing a quantitative role at an investment bank. However, after much soul searching, I decided on a masters in Data Science. This was tough initially due to my lack of computer science background, but my thesis on detecting biomarkers for Parkinson’s disease motivated me to keep going and persevere. After completing my Master’s degree, I joined KPMG UK as a consultant.
During my time at KPMG, I worked on multiple projects across various data pillars, including data engineering, business intelligence, data science, and cloud. This allowed me to develop a holistic and comprehensive view of the data journey, picking up new skills in SQL, R, PowerBI, Star Schema dimensional modelling, VBA, and creating machine learning (ML) models for location analytics in R, Python, and building on Azure. I also worked across different industries, such as retail, healthcare, and supply chain, to name a few.
In addition to technical and industry knowledge, I learned the untaught soft skills that you tend to pick up when you start working in the fast-paced consulting environment. Working with some really good people, and having a super supportive mentor who played an instrumental role in guiding and shaping my early career was a valuable experience.
I still remember their advice:
“Sometimes you need to be patient and do things that you do not necessarily enjoy in order to get to where you want to be or do what you like”.
This struck a chord with me, and even though it was tough at the time, in hindsight, I fully relate to it. In the corporate world or life in general, you’ll go through moments that make you question why and what you’re doing. But it’s essential to do things that are out of your comfort zone, or that seem monotonous, in order to reach your desired destination.
VENTURING INTO SALES ENGINEERING
I first heard about sales engineering from a shared connection who worked at Google Cloud and was kind enough to refer me. Although I had no experience with Google Cloud, I knew that it was one of the top three public cloud providers, alongside AWS and Azure. My knowledge in cloud computing came from my work at KPMG, where I focused on Azure and obtained a few certifications. With my consulting experience and data background, I found pre-sales to be the perfect fit for me. I loved that it was at the intersection of people, strategy, and technology. I could help customers by understanding their needs and advising them on solutions that leveraged technology to drive business value.
At Google Cloud, I worked as a Customer (Sales) Engineer Specialist. The main difference between generic pre-sales and a specialist was the product focus and knowledge. As a specialist, I was expected to have a deep understanding of the domain I was responsible for. I was the Data Analytics and ML Specialist for the Digital Natives cluster in the UK and Ireland. My main role was to guide our customers on best practices using Data Analytics/ML on Google Cloud, demonstrate the Data Analytics/ML capabilities, and show how they were relevant for their specific use case. In addition, being part of Digital Natives, I had the opportunity to work with a number of startup unicorns spanning FinTech, MedTech, retail/e-commerce, MarTech, and Technology. Throughout my time at Google, I led or contributed to several initiatives with a focus on community events and public speaking that also included collaborating with various Google Cloud partners.
I had an amazing time at Google, both on a professional and personal level. I am immensely grateful for having had that opportunity and for having worked with some of the smartest and googliest people I know. The best part of having worked at Google is the lifelong network. There is a large alumni community of Xooglers (ex-Googlers) who help each other throughout their careers. Recently, the community has been proactively helping people impacted by the mass tech layoffs.
WINNING HEARTS AND MINDS WORKING AT SNOWFLAKE
Though I really enjoyed Google, I knew that I wanted to do something different in terms of my role. I wanted to retain my core specialist skills in data/ML, while being more closely aligned to the accounts and customers I was working with. As a Pre-Sales Specialist, although I was aligned at an opportunity level (if anything related to data/ML), I was not involved at the full account level unless it was solely focused on data/ML.
Typically, moving to a generalist role meant I would be covering the entire cloud portfolio, which meant I would not be as focused on data/ML as I would have liked to be. When I got the opportunity to work in a Sales Engineering role at Snowflake, I discovered that not only do they have an amazing product, but also the company values and the team are exceptional and aligned with what I was looking for. I was excited to be part of Snowflake, especially with the organisation growing at such a rapid scale and expanding its ML capabilities.
Snowflake is a data platform built in the cloud, which has a unique, highly scalable architecture that supports multiple workloads. Snowflake originally started with the goal of disrupting data and analytics silos, and then expanded into collaboration with data sharing, and most recently has focused on breaking down ML and development silos with products like Snowpark, the Native App Framework (currently in preview), Streamlit, and more. I love the fact that I get to work closely with Snowflake customers, while also building my expertise across the data and ML lifecycle. The scope is only getting bigger, and with Snowflake the best part is that everything is connected to data at its core.
As a Sales Engineer at Snowflake, I am aligned to various customer accounts and I get to be a part of the full customer journey (which is what I was looking for compared to my previous role). I now work closely with customers end-to-end, while also using my technical knowledge in data analytics and data science. I currently work with customers in the energy space who are leveraging data and/or ML to make data-driven decisions and realise value. Besides data and tech, it’s been fascinating to immerse myself and learn how the energy industry works and the interconnected channels. In addition to my day-to-day activities, I have been involved in leading or contributing to multiple initiatives including data science and MLOps enablement for UK and Ireland Sales Engineering teams, community events such as the Snowflake Python meetup with London Python, and speaking at Snowflake developer events (BUILD.local), amongst other events.
“A 2021 survey from Deloitte reinforced that a diverse team is better equipped to address the biases in data and AI to build efficient systems.”
BEING A WOMAN IN DATA, AND TECH
With the growth of big data and ML, there are definitely more opportunities and job prospects for women. Despite this growth, it is disappointing that the gender gap is still very much prevalent, especially in technical roles. Women only account for 26% in IT (womenintech. co.uk, 2023) and only 20% within Data and AI roles (Alan Turing Institute, 2023).
I have been incredibly fortunate to have some amazing mentors, managers, and peers who have been instrumental in my development and success at work. It is very important to have a strong support system and network to be successful at work and for career growth.
A trait that I observed in women across the workforce is that they sometimes underplay their accomplishments and skills and don’t speak up as often. My advice for young women starting out in tech would be to not be afraid to reach out to their network (build a network of supporters in the first place), ask for help, share thoughts (speak up), and celebrate success. It is important to remember that one person cannot know everything, so it’s okay to ask for help. It is also important to acknowledge and accept that we know more than we think we know, and to share our thoughts and views at the appropriate time.
As a woman in data, I have been in numerous situations where I tend to be the only woman, and sometimes the only woman of colour. This doesn’t bother me. I have been fortunate enough to be surrounded by very supportive colleagues throughout my career. Having said that, I must acknowledge that there were moments when I felt that I wasn’t being taken seriously because of my gender and/or age. This made me doubt myself and my capabilities, and this can have quite a negative impact – especially in the early career stages. Unfortunately, I also quickly learned to navigate patronising behaviour towards me. I now understand my boundaries, and how to stand up for myself to avoid situations like this in the future. I am often reminded of this analogy: whereby in the majority of restaurants, the bill is naturally presented to the man first when they’re dining with a woman. It sometimes happens in organisations too that when a man and woman work together on a project, there’s an assumption that the man is the lead. Many of these experiences have shaped me. I work even harder to prove myself and always try to be on top of my game because I have this fear of not being taken seriously. It is a sad state of affairs that women sometimes seem forced to prove themselves again and again, because of various in-built assumptions and stereotypes prevalent in society.
I believe that it takes conscious learning and unlearning by everyone to break these biases and perceptions about women and the types of jobs they do, alongside the contributions they make. Our lived experience is different from men and a diverse and inclusive team can massively contribute to better problem solving and drive innovation. A 2021 survey from Deloitte reinforced that a diverse team is better equipped to address the biases in data and AI to build efficient systems. We do see progress compared to previous years, however, there is definitely room for improvement in the diversity, equity, and inclusion space. It is good to see most companies now investing time and resources to make their organisation more inclusive. Creating a safe space where you are encouraging collaboration and curiosity, recognising contributions, not penalising people for speaking up, and offering support goes a long way in creating a culture of inclusivity and belonging. Secondly, although it is difficult and it doesn’t come intuitively to most people, practicing empathy contributes to fostering an inclusive culture – especially in data and technology.
Last but not the least, it’s critical to remember that representation matters. I have noticed the lack of women/women of colour role models in leadership roles across both the data and technology landscape. In fact, most of my mentors and managers at work have been men (although strong allies!). Having women leaders share their stories and time would significantly encourage more women to pursue careers in data and tech. Whether through a STEM degree or a non-linear path into tech, having relatable role models and mentors would be an inspiration.
