How I Became a QlikSense Data Engineering Developer: Jenny's Journey

Hi, I'm Jenny, and I'd like to share my journey of becoming a QlikSense Data Engineering Developer. It was a challenging but rewarding path, and I hope my experience can help you if you're aiming for a similar goal.

Step 1: Understanding the Basics of Data Engineering

When I started, I knew I needed a solid foundation in data engineering. I took online courses to learn about data warehousing, ETL processes, and SQL. Platforms like Coursera and Udemy were incredibly helpful, and books like "Data Warehousing for Dummies" and "SQL for Dummies" became my best friends.

Step 2: Learning QlikSense Basics

Next, I focused on QlikSense. I began with the basics, such as navigating the interface, creating simple visualizations, and understanding data connections. QlikSense's official tutorials and YouTube guides were my go-to resources. I also spent a lot of time on Qlik Community forums, which were great for troubleshooting and tips.

Step 3: Developing Data Modeling Skills

Data modeling was a crucial step. I studied different techniques and practiced creating data models in QlikSense. The data modeling courses offered by QlikSense and various online tutorials were invaluable. I also read a few books on data modeling concepts to deepen my understanding.

Step 4: Mastering Advanced QlikSense Features

To really excel, I knew I had to master advanced features. I dived into set analysis, advanced scripting, and performance tuning. This was challenging, but advanced QlikSense courses and the official help guide were incredibly helpful. I also leaned on online communities for support.

Step 5: Learning Data Engineering Tools and Technologies

Expanding my skill set was next. I learned Python and R for data manipulation, Hadoop and Spark for big data processing, and got familiar with cloud platforms like AWS and Azure. MOOCs, books, and online tutorials were my main resources here. Each new tool opened up more possibilities for my projects.

Step 6: Gaining Practical Experience

Nothing beats hands-on experience. I built data pipelines, created dashboards and reports, and optimized data workflows. I took on personal projects, freelance gigs, and even contributed to open-source projects. Each experience taught me something new and made me more confident in my skills.

Step 7: Obtaining QlikSense Certification

To validate my skills, I aimed for QlikSense certification. I studied hard, using practice exams and study guides. The certification courses and materials provided by QlikSense were essential, and I spent a lot of time on practice exams available online.

Step 8: Networking and Continuing Learning

Networking played a big role in my journey. I joined data engineering communities and attended industry conferences. Staying updated with the latest trends was crucial, and continuous learning through courses and workshops kept me sharp. Professional groups and online forums were excellent for networking and knowledge sharing.

Step 9: Applying for Jobs and Building a Career

Finally, it was time to apply for jobs. I tailored my resume to highlight my QlikSense and data engineering skills and prepared for interviews. Job boards and company websites were my main sources for job listings. I also used resume writing services and career counseling to polish my applications. After a few interviews, I landed my dream job.

Conclusion

Becoming a QlikSense Data Engineering Developer was a journey of continuous learning and practical application. It took a lot of dedication, but it was worth it. If you're on a similar path, my advice is to stay curious, keep learning, and don't be afraid to take on new challenges. Good luck!