From Non Tech to Tech: Subha Saha's Amazon Transition Journey

In this interview, we delve into the journey of Subha Saha, a Data Engineer at Amazon. Having spent close to a decade at the tech giant, Subha has successfully transitioned from a non-technical role to a thriving career in data engineering.

Subha shares valuable insights into his career path, highlighting the pivotal moments that led him to embrace technology, the challenges he faced, and the valuable lessons he learned along the way. We'll also explore the role of mentorship and training in his professional development and the impact of his work at Amazon.

Through Subha's inspiring story, we gain a deeper understanding of the world of data engineering and the opportunities it offers for those seeking a career transition. 

Excerpts: 

Navigating the Path

What was the pivotal moment or realization that drove you to transition from a non-technical role to a tech career at Amazon? How did you identify that data engineering was the right path for you? 

I have been with Amazon for close to a decade now. I joined as an Intern in the Merchant Risk Investigations team and transitioned into a full-time role of Investigation Specialist. The pivotal moment for me was during my time as a business analyst on the Alexa team. 

Despite coming from a non-tech background, I was fascinated by the impact of data and technology in driving decisions and innovation. Working with SQL and gradually getting exposed to more advanced tools, I realized that I enjoyed the challenge of making sense of data and building solutions that others could rely on.

The more I delved into data analysis, the more I recognized that data engineering was a field where I could combine my analytical skills with a technical approach to problem-solving. The decision to transition into data engineering felt natural as I was already contributing to the creation of data-driven insights, and I wanted to expand my capabilities further. 

Switching to a technical field often involves a steep learning curve. How did you approach learning new concepts in data engineering, and what resources or techniques proved most effective for mastering complex topics?

Approaching the learning curve in data engineering required a mix of structured learning and practical application. I began by focusing on the foundational skills, like SQL and understanding data pipelines, which were essential for my role.

Amazon provides access to a wealth of resources, including training, skill building, internal wiki site that contains comprehensive information about Amazon, an internal video site ‘Broadcast,’ where employees can post videos so that others can learn everything from specific coding practices to how to write a persuasive ‘’working backward’’ document and so on, and most importantly, mentorship.

I found that working on real projects, under the guidance of experienced mentors, allowed me to grasp complex topics more effectively than theoretical learning alone. I also made it a habit to engage in self-directed learning through platforms like AWS training courses and hands-on practice, which solidified my understanding of key concepts.

How did Amazon's mentorship and training programs contribute to your development? Can you share specific instances where these resources played a critical role in your career transition?

Mentorship is available in many forms at Amazon, through informal networks established by employees, programs that took root in smaller orgs, or the more formal Amazon Mentoring Program (AMP).

AMP is available to every Amazon employee and connects prospective mentees to mentors, providing structure to their relationships, creating opportunities for skill building, and helping people grow their networks inside Amazon.  

One key instance was when I was paired with a mentor through AMP during a challenging personal time. My mentor, who was a seasoned business analyst, not only helped me navigate my work but also taught me the tools and techniques needed for efficient data analysis.

This relationship was a turning point in my career as it provided me with the technical guidance and confidence to transition into a more tech-focused role. Additionally, the opportunity to work on cross-functional projects allowed me to learn from colleagues in different teams, further broadening my skill set.

Simplilearn's 2023 consumer survey highlights the importance of resilience and dedication in career transitions. How have these qualities played a role in your journey, and how do you maintain them in the face of challenges?

Resilience, persistence and dedication have been the cornerstones of my journey. By leveraging my existing skills, acquiring new knowledge, and building a strong network, I could successfully navigate transition from a non-tech into a tech role.

Transitioning to a technical role was challenging, but I remained focused on the long-term goal of becoming proficient in data engineering. I encountered numerous setbacks, especially when learning new technologies, but each challenge was a learning opportunity. With the right approach and mindset, transitioning into a tech role is not only possible but also rewarding. 

I maintained resilience by setting small, achievable goals, seeking feedback, and continuously reminding myself of the progress I was making. Dedication came from a genuine interest in the work I was doing and the support I received from my peers and mentors, which kept me motivated even during difficult times. 

Could you highlight a few key projects you've worked on at Amazon that reflect your growth and the impact you've made? How did these projects shape your career trajectory?

One of the key projects I worked on involved creating internally built tools that are now widely used across Amazon teams. These tools have streamlined processes and provided teams with better access to critical data, which has had a significant impact on their efficiency.

Another notable project was my work with the Whole Foods team few years ago, where I developed WBR (Weekly Business Review) reports and dashboards that are currently in use. 

These experiences not only expanded my technical skills but also taught me the importance of building scalable solutions that can benefit multiple stakeholders. I have worked on building big data pipelines for last mile team that helps team to monitor and help in marinating quality of customer delivery. These projects have been pivotal in shaping my career by giving me the confidence to tackle larger, more complex challenges in the future.

Beyond technical skills, what personal development practices have been crucial to your career success? How have you worked on skills like leadership, communication, or problem-solving?

Personal development has been just as important as technical growth in my career. I might not have had a background in programming or computer science, but I possessed transferable skills that are also valuable in the tech field. My skills related to problem-solving, critical thinking, communication, and project management were all applicable to tech roles as well.

I’ve consistently worked on improving my communication skills, recognizing that effectively conveying ideas is crucial in any role. I took every opportunity to present my work, whether in team meetings or more formal settings, which helped me become more articulate and confident. Leadership is another area I’ve focused on, especially as I started mentoring colleagues from non-technical backgrounds.

By sharing my experiences and guiding them through their transitions, I’ve honed my leadership abilities. Problem-solving, particularly in the context of data, has been a key focus, and I’ve approached it by breaking down complex issues into manageable parts, using a combination of technical knowledge and creative thinking to find solutions.