Explore the inspiring journey of Nino Kikvadze, a driven Software Engineer at EPAM Georgia, who transitioned from a non-IT background to a thriving career in technology. Through her dedication and passion for data, Nino embraced EPAM’s Data Analytics Engineering program and now plays a crucial role in shaping innovative solutions.
Choosing the Data & Analytics path
Nino's journey into Data & Analytics was not straightforward. Coming from a non-IT background, she had always been fascinated by the power of data and how it drives business and technology decisions. Her curiosity led her to explore various online resources, where she eventually discovered EPAM's Data Analytics Engineering program. Its beginner-friendly approach seemed like the perfect entry point into the field.
She was inspired to enroll in EPAM's Data Analytics Engineering program for several key reasons. As a leading software engineering company, EPAM’s focus on emerging technologies and best practices was a major draw. The program offered a comprehensive overview of Data Analytics and Engineering, covering essential topics like Data Quality, Data Integration and Data Visualization, all crucial to her career goals. Nino appreciated the program's emphasis on hands-on experience and practical application of concepts. Additionally, the support from experienced mentors was something she greatly anticipated.
Knowing I would receive guidance and feedback gave me confidence that I’d be well-supported in making this career shift.
Another significant factor was the prospect of future employment opportunities. EPAM’s strong industry connections and its focus on equipping students with practical, in-demand skills reassured Nino that completing the program would open doors to meaningful career prospects.
Impactful aspects of training
The program was divided into two parts. The first part focused on SQL and PostgreSQL, where Nino and her peers had weekly tasks that helped sharpen their database skills. This hands-on work with SQL gave her a solid foundation.
The second part of the program was more diverse, covering various modules like Python, Data Warehousing (DWH), AWS, Data Quality and Data Visualization. While each module offered unique insights and skills, the Data Warehousing module stood out the most for Nino.
In that module, they had the opportunity to build their own data warehouse, which was a game-changer for Nino. She and her peers designed ETL processes, built data source schemas and managed the entire flow of data. It was fulfilling for her to see how everything came together and to gain a real-world understanding of the complexities involved in data warehousing.
Accepting challenges
One of the biggest challenges Nino faced was during the Data Warehousing module, specifically when she had to create a procedure for Slowly Changing Dimension Type 2 (SCD2). As a beginner, SCD2 was tricky for her because it involves managing historical data while tracking changes over time. She had to ensure that old records were preserved while new records reflected the latest updates.
To overcome this challenge, Nino reviewed the course materials and conducted additional research to deepen her understanding of SCD2. She also sought guidance from mentors and peers, whose advice proved invaluable. Nino practiced by working on smaller tasks and created sample datasets to test her SCD2 implementation. Eventually, all that practice paid off, enabling her to successfully design and implement the procedure.
Bridging theory and practice
The internship at EPAM complemented Nino's learning path in several impactful ways. During her Data Analytics Engineering training program, she developed a strong theoretical understanding of key concepts, while the internship provided a practical application of these concepts.
For example, while the training covered foundational knowledge in areas like SQL and data warehousing, the internship allowed Nino to engage in more hands-on tasks involving cloud engineering, data quality and data visualization. Working on real-life challenges during the internship gave her the opportunity to apply what she had learned in a practical setting, helping her to better understand and tackle complex data scenarios.
Transitioning to a new role
The transition from learning to Nino's role as a Software Engineer went smoothly. After completing the Data Analytics Engineering program, she went through technical interviews, which provided a great opportunity to demonstrate everything she had learned. Once the interviews were successfully completed, Nino was assigned to her first project and began working with her new team.
Typical working day
Nino's typical day is quite dynamic. It begins with meetings where her team discusses ongoing projects and aligns on priorities. Afterward, she dives into tasks related to data engineering, which may involve writing or optimizing code, designing data pipelines or performing data transformations using tools like Spark and Databricks.
She also handles troubleshooting and resolving data quality issues and often participates in QA calls to review new features. Deploying new code or features is another key part of her day, ensuring everything runs smoothly.
The work can be challenging, especially with tight deadlines, but Nino finds it rewarding. She enjoys the variety and the constant learning that comes with the job.
Skill application
The most valuable technical skills Nino gained during her training are the fundamentals of Data Warehousing (DWH). Although her current work primarily involves using a Delta Lakehouse rather than a traditional DWH, the foundational knowledge of DWH has proven incredibly useful. It provided her with a solid understanding of data modeling, schema design and data management. Additionally, the AWS module from her training has been beneficial, even though her current role focuses on Azure. The principles and best practices she learned about cloud data platforms gave her a strong grasp of cloud architecture and data management, which translates well across different cloud environments.
Goals and skill advancement
Nino aims to deepen her knowledge of data architecture and design principles, with a specific focus on building scalable data systems and workflows. She also plans to stay up to date with the latest developments in data engineering by attending webinars, reading technical publications and engaging with industry resources. Nino believes that continuous learning will help her remain at the forefront of the field and adapt to evolving trends and challenges.
Beyond technical skills
In addition to technical skills, Nino recognizes the importance of soft skills like effective communication and time management. The ability to convey complex data-related concepts in a clear and understandable manner to stakeholders is essential. Balancing multiple tasks and projects efficiently is crucial for meeting deadlines and managing workloads. Strong time management skills help her prioritize tasks and work effectively.
Adaptability is another key skill, especially as technology evolves rapidly. The ability to embrace new tools, frameworks and methodologies is vital for staying relevant. Being open to change and eager to learn new skills helps Nino remain effective in a constantly shifting field.
Resource recommendations
Nino prefers reading books and tackling challenges as part of her learning process. She finds official documentation for tools to be particularly helpful. Here are some resources she recommends:
- Databricks Documentation: Guides on using Databricks for data engineering and analytics
- Apache Kafka Documentation: Detailed information on Kafka’s architecture and operations
- Apache Spark Documentation: Resources for learning about Spark’s features and capabilities
For books, she suggests:
- Kafka: The Definitive Guide by Neha Narkhede, Gwen Shapira and Todd Palino
- Automate the Boring Stuff with Python by Al Sweigart
- The Data Warehouse Toolkit by Ralph Kimball and Margy Ross
Tips for beginners in Data Analytics Engineering
Nino emphasizes the importance of a few key strategies for success:
- Master the Fundamentals: Start with a solid grasp of fundamental data concepts such as data structures, data quality and data transformation. Understanding how data is stored, manipulated and analyzed is crucial for any data analytics role.
- Work on Projects: Apply skills to real-world projects, including analyzing datasets, building data pipelines or creating visualizations. Hands-on experience will reinforce learning and enhance problem-solving abilities.
- Stay Curious: Cultivate a genuine interest in data and a strong desire to learn. Data analytics is an ever-evolving field, so being open to new tools, techniques and technologies will support personal growth and adaptability.
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