Issue 010 - Meet Erfan Hesami
I realized that data analysis often intersects with Data Engineering, especially in roles that require descriptive analytics or hypothesis testing before processing data for downstream stakeholders.
Meet Erfan Hesami, an innovative Data Engineer at Airmaster, whose journey into the data field has been driven by curiosity and a desire for growth. Starting with a foundation in IT Engineering, Erfan discovered his love for working with data during his bachelor’s degree.
This passion grew stronger as he pursued a master’s degree in Business Analytics with a focus on Data Science, propelling him into roles where he could leverage data to solve meaningful business problems.
What are some key skills that are often overlooked in your field of data?
Domain knowledge is often overlooked. Understanding the industry or business you’re working in helps connect data to real-world problems, enabling meaningful insights and decisions.
Additionally, understanding business concepts, such as how a company generates revenue and how solving specific problems impacts the business is invaluable. This context allows data professionals to create solutions that directly contribute to business success.
What inspired you to pursue a career in data, and how has your motivation evolved over time?
My journey into data began during my bachelor's degree in IT Engineering in 2011, where I studied subjects like data structures, AI, SQL, networking, and software engineering. After graduating, I had several career options, including web development, software engineering, and network engineering. I started as a junior software engineer but quickly developed a fascination with how data generated by applications could be used to solve business problems.
This curiosity led me to pursue a master’s degree in Business Analytics with a focus on Data Science. As I progressed, my interest in exploring and analyzing data to uncover insights grew stronger, landing me my first role in Data Analytics.
Over time, I became increasingly curious about how data is collected, ingested, and stored. I collaborated with my team and other departments to learn more, which deepened my passion for Data Engineering. I realized that data analysis often intersects with Data Engineering, especially in roles that require descriptive analytics or hypothesis testing before processing data for downstream stakeholders.
How do you balance the technical aspects of your role with the need for creative problem-solving?
Balancing technical execution with creative problem-solving starts with clearly defining the problem. I begin by understanding the problem, why it exists, and how it can be solved. I also confirm the business value of addressing the issue with stakeholders.
Once I have clarity, I choose the appropriate tools or methods to tackle the problem. This ensures technical solutions are both effective and aligned with business objectives while fostering innovative thinking.
Can you share a pivotal moment in your career that significantly shaped your approach to data?
A pivotal moment came when I worked with a colleague named Adebayo, who emphasized the importance of mastering fundamentals and developing strong problem-solving skills. He taught me that not every problem has a single solution; sometimes, multiple approaches or steps are required. This mindset encouraged me to explore adaptable solutions rather than relying on one tool or method.
I was also fortunate to have supportive managers who encouraged me to learn new skills and broaden my expertise. These experiences significantly shaped how I approach data, emphasizing flexibility and continuous growth.
What do you love outside of data?
I love music. I enjoy listening to different genres, attending concerts, and occasionally playing guitar and singing. I also enjoy spending time in nature, appreciating its beauty, and reflecting. It’s a great way to relax and recharge.
How do you foster a culture of data-driven decision-making within your team or organization?
Building a culture of data-driven decision-making starts with trust and collaboration. Establishing strong relationships with colleagues and stakeholders is crucial. Once trust is built, I demonstrate how data can solve problems and improve decision-making.
By involving stakeholders in the process and seeking their input, I ensure that they see the value of data-driven approaches. This collaborative effort makes it easier to gain their support and build a strong foundation for data-driven decision-making.
What role does mentorship play in your career, and how have mentors influenced your journey?
Mentorship has played a vital role in my career. I’ve been fortunate to have amazing mentors who guided me with their advice and experiences. They helped me navigate challenges, improve my technical skills, and understand the importance of lifelong learning.
I believe in paying it forward by sharing my experiences with others. Even small insights can be valuable, and the act of helping someone can have a profound impact. Supporting the community helps us grow and succeed together.
Can you discuss a project where data insights led to unexpected outcomes or revelations?
One project involved analyzing data that initially appeared reliable based on the exploration of tables, columns, and definitions. I trusted the data and built my analysis around it, but I later discovered that my understanding of the data was flawed.
This experience taught me never to take data at face value. Even if the data looks accurate, it’s essential to test assumptions and validate hypotheses. I learned the importance of double-checking insights with colleagues who are familiar with the data. Proper documentation, data dictionaries, and lineage are invaluable, but in their absence, piecing everything together requires diligence and caution.
Can you reflect on a setback in your data career and what you learned from it?
Early in my career, I was eager to learn new tools and pursue certifications, jumping from one to another. Eventually, I realized this approach wasn’t effective. Focusing too much on tools without understanding the fundamentals left gaps in my knowledge.
I learned that mastering core concepts is far more valuable, as tools come and go, but strong foundational knowledge remains relevant. Strengthening the fundamentals makes adapting to new tools much easier and more efficient.
What resources (books, courses, tools) do you recommend for people to level up their skills?
The resources depend on the area of data you want to focus on. Here are some general recommendations:
Books:
Courses:
Certifications:
Focus on mastering fundamentals, learning open-source tools, and staying updated through industry leaders like Joe Reis, Zach Wilson, and Chad Anderson via LinkedIn and Substack.
Key Takeaways
Curiosity Drives Growth: Erfan’s relentless curiosity has shaped his career, pushing him to explore the intricacies of data collection, processing, and storage.
Master the Fundamentals: Erfan emphasizes that tools may change, but a strong foundation in core concepts is the key to adaptability and long-term success.
Collaboration Builds Value: By working closely with teams and stakeholders, Erfan ensures that data solutions align with business objectives and create real impact.
Mentorship Fuels Success: Guided by incredible mentors, Erfan has grown into a well-rounded data professional and is committed to sharing his knowledge with others.
Validate Your Data: Erfan highlights the importance of testing assumptions and validating data to avoid errors and ensure the reliability of insights.
ICYMI: Catch Up on Previous Issues
Best,
Ayoade Adegbite
Founder of Behind the Data