Real Interview Q&A for Senior Data Engineer | Surfalytics

Gain a unique perspective on the technical and problem-solving skills expected of senior data engineers in this very little edited (removed pauses and small talk) Q&A session with me. This interview delves into real-world scenarios and challenges, offering invaluable insights for both aspiring and experienced data engineers.

Key Topics Explored:

Databricks Expertise: Discuss practical experiences with Databricks, from implementation to optimization.
Spark Coding Best Practices: Share preferences for functions vs. classes, PySpark dataframes vs. Spark SQL, and explain rationale behind these choices.
Spark Performance Troubleshooting: Detail a systematic approach to identifying and resolving performance bottlenecks in Spark queries.
Complex Join Optimization: Address strategies for optimizing joins involving large tables with skewed data distributions.
Slowly Changing Dimensions (SCD): Explain different SCD types, provide examples of their use, and discuss implementation considerations.
Key Management: Compare and contrast surrogate and natural keys, highlighting appropriate use cases for each.
Data Quality Assurance: Outline techniques and best practices for ensuring data quality throughout the pipeline.
Requirements Gathering: Describe collaborative processes for defining clear and comprehensive project requirements.
Churn Calculation: Explain the formula and methodology used to calculate customer churn, emphasizing relevant metrics.
Fuzzy Text Matching: Discuss techniques for joining datasets based on textual columns that may contain slight variations.
This video offers a rare opportunity to witness a senior data engineer's thought process and problem-solving approach in real time. Whether you're preparing for interviews, seeking to expand your knowledge, or simply curious about the profession, this session is an invaluable resource.

Target Audience: Data engineers of all levels, data professionals, hiring managers, and anyone interested in the field.

#DataEngineering #TechnicalInterview #Spark #Databricks #BigData #DataQuality #CareerDevelopment

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Timecode:
00:00 - Start
00:24 - About my Databricks experience
04:00 - The size of data I worked with
04:50 - About my data team
06:38 - How I calculate churn
08:27 - About proactivity of team members
11:36 - How do I code for Spark
14:36 - Spark performance troubleshooting
17:12 - Question about skewed data
18:58 - My biggest challenge
20:42 - How do handle SCD
24:36 - What about keys?
25:58 - Quality of data
28:27 - Compare non equal text columns
29:44 - My questions to the interviewer

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What is Surfalytics?

Inspired by West Coast surfing spots 🏖️ and Pacific Ocean vibes 🌊. Created to help you start a new career in the data analytics space, and develop data engineering and analytics skills through coaching. It will teach you not just dry skills, but will keep your focus on delivering significant value to businesses in the analytics realm as well as help to get fair compensation 💰 for the work you’re passionate about ❤️‍🔥.

The goal of Surfalytics is to assist you in achieving one of the following:

🏄‍♂️ Land your first job in the data industry with literally zero experience. I have accomplished this many times across the globe.

🏄 Advance from a middle-level role to a senior position (as an Analyst or Engineer).

🏄‍♀️ Transition from a non-technical Analyst role to a technical Engineer role.

Moreover, we will focus on creating a highly competitive CV and securing top job offers. We will not consider any lowball offers, focusing only on top-tier companies and well-paid opportunities.

Finally, Surfalytics is a results-driven community with a very narrow focus, resulting in a high return on investment (ROI). Here, ‘investment’ does not mean money but your time. I am literally fighting for your attention to encourage you to study and work hard, instead of watching Netflix or playing video games.

This is the best YouTube channel for Data Analytics and Engineering. You will patch up a lack of knowledge and get new experience and tips to build a Data Analyst roadmap or Data Engineer roadmap for yourself.

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