Introduction of the interview changed a bit

I didn't say that the best engineers started as data analysts.

sections/01-Introduction.md

@@ -307,7 +307,7 @@ That's why companies are looking for people with experience of tools in every pa
 
 This interview with Andreas  on Jon Krohn's Super Data Science podcast delves into the intricacies of data engineering, highlighting its critical role in the broader data science ecosystem. Andreas, calling from Northern Bavaria, Germany, shares his journey from a data analyst to becoming a renowned data engineering educator through his Learn Data Engineering Academy. The conversation touches upon the foundational importance of data engineering in ensuring data quality, scalability, and accessibility for data scientists and analysts.
 
-Andreas emphasizes that the best data engineers often have a background in data analysis, which equips them with a deep understanding of the end user's needs. The discussion also explores the essential tools and skills required in the field, such as relational databases, APIs, ETL tools, data streaming with Kafka, and the significance of learning platforms like AWS, Azure, and GCP. Andreas highlights the evolving landscape of data engineering, with a nod towards the emergence of roles like analytics engineers and the increasing importance of automation and advanced data processing tools like Snowflake, Databricks, and DBT.
+Andreas emphasizes that the best data engineers often have a background in the companies domain/niche, which equips them with a deep understanding of the end user's needs. The discussion also explores the essential tools and skills required in the field, such as relational databases, APIs, ETL tools, data streaming with Kafka, and the significance of learning platforms like AWS, Azure, and GCP. Andreas highlights the evolving landscape of data engineering, with a nod towards the emergence of roles like analytics engineers and the increasing importance of automation and advanced data processing tools like Snowflake, Databricks, and DBT.
 
 The interview is not just a technical deep dive but also a personal journey of discovery and passion for data engineering, underscoring the perpetual learning and adaptation required in the fast-evolving field of data science.
 
@@ -327,7 +327,7 @@ A: The transition involves gaining a deep understanding of data pipelines, learn
 A: Essential skills include proficiency in SQL, experience with ETL tools, knowledge of programming languages like Python, and familiarity with cloud services and data processing frameworks like Apache Spark. Tools like Kafka for data streaming and platforms like Snowflake and Databricks are also becoming increasingly important.
 
 **Q: Can you elaborate on the emerging role of analytics engineers?**
-A: Analytics engineers focus on bridging the gap between raw data management and data analysis, working closely with data warehouses and using tools like DBT to prepare and model data for easy analysis. This role is pivotal in making data more accessible and actionable for decision-making processes.
+A: Analytics engineers focus on bridging the gap between raw data management and data analysis, working closely with data warehouses and using tools like dbt to prepare and model data for easy analysis. This role is pivotal in making data more accessible and actionable for decision-making processes.
 
 **Q: What advice would you give to someone aspiring to become a data engineer?**
 A: Start by mastering the basics of SQL and Python, then explore and gain experience with various data engineering tools and technologies. It's also important to understand the data science lifecycle and how data engineering fits within it. Continuous learning and staying updated with industry trends are key to success in this field.