Saxon global, inc. Data Engineer Interview Guide

When addressing data quality issues, it's crucial to start by identifying specific problems, such as inconsistencies or incompleteness in datasets. For instance, in a previous project, I noticed discrepancies in customer data that affected our analytics. I initiated an investigation by analyzing data sources, which revealed that the issue stemmed from multiple entries being created in different systems. To resolve this, I implemented a data cleansing routine and established validation rules to prevent future occurrences. This not only rectified the existing data but also enhanced our overall data governance practices.

Effective collaboration with non-technical stakeholders is essential for successful data projects. For example, while working on a data integration project, I set up regular meetings with business representatives to understand their needs. I used visual aids and simplified language to explain technical concepts. By translating their requirements into actionable tasks, I was able to align our technical solutions with business goals. Continuous feedback loops ensured that the final data solution met their expectations and served their needs effectively.

Adapting to changing requirements is a common challenge in data engineering. In one instance, halfway through a project to build a data warehouse, the business decided to change the focus from historical reporting to real-time analytics. I quickly organized a team meeting to reassess our project plan and identify the necessary tools and technologies for real-time data processing. We pivoted towards using Azure Data Factory and enhanced our data pipeline architecture. This adaptability allowed us to deliver a solution that exceeded the revised expectations.