Daniel’s academic journey took him to the realm of analytical chemistry, where he was in the final year of his PhD. However, as his PhD neared its end, he realised his desire to apply his skills in a more tangible and impactful way. This realisation sparked his interest in the correlation between chemistry and technology, creating a niche opportunity for him.

Driven by knowledge and the desire to apply his skills, Daniel’s motivation to pursue a career in data engineering was fuelled by the potential for growth and use cases. He recognised the demand for data engineers and the exciting prospects the field held for him.

Navigating the learning curve at the Data Academy:

Transitioning from academia to the technical landscape of data engineering presented unique challenges for Daniel. While he had self-taught Python programming skills, the fundamentals of Azure and data pipelines were new territories. Nevertheless, he found the learning experience at the Data Academy to be enlightening, bridging the gap between his existing skills and the essential knowledge required in the field.

Overcoming challenges:

Balancing his training at the Data Academy with the final stages of his Ph.D. posed a significant challenge for Daniel. However, he appreciated the clear work boundaries set by the academy, which allowed him the freedom to excel in both areas. The academy’s challenges were carefully curated, striking the right balance between difficulty and attainability.

The support and mentorship of the Data Academy:

Daniel expressed his gratitude for the exceptional mentorship and support he received at the Data Academy. Instructors like Dylan and Maria were always available to provide guidance and assistance, going the extra mile to ensure his success. The academy’s emphasis on well-being and personal growth played a vital role in nurturing Daniel’s development as a data engineer.

Skills and knowledge that’ll lead him to an enterprise-ready Data Engineer:

Beyond technical knowledge, Daniel gained practical skills that made him more versatile as a data engineer. Learning Azure DevOps, Active Directory, Key Vault, Data Lakes, and Functions expanded his toolbox. Additionally, he acquired crucial soft skills such as working in agile methodologies, stakeholder engagement, and effective communication

While still undergoing training at the Data Academy, Daniel’s weekly challenges and group sessions on creating end-to-end cloud solutions have set the stage for his transition into an enterprise-ready cloud data engineer. The comprehensive curriculum ensures that graduates are well-prepared to tackle real-world projects and deliver value to organisations. Daniel’s dedication and hard work paid off when he mastered the use of Azure functions. Leveraging this knowledge, he successfully automated a function for a web store, showcasing his ability to apply his skills to practical scenarios.

Our unique approach to soft skills:

The Data Academy’s unique gamified learning sessions provided Daniel with increased confidence and an opportunity to develop his soft skills. By facilitating personal growth and fostering effective teamwork, the academy equips graduates like Daniel with the essential qualities needed to thrive in professional environments.

Looking ahead, Daniel aspires to explore the realm of data science, delving into machine learning and modelling big data sets to derive valuable predictions. With his foundation in data engineering and the support he’s well-positioned to pursue these ambitions within Elastacloud.

Learn more about our Data Academy programme here