Sandip Ghosh’s Post

Data Engineers, are you struggling to get your message across in interviews?   I met with a Data Engineer yesterday for some interview prep who said: "I can overtalk and lose my point in interviews"   Here’s some of the advice I gave to tackle this specific problem: Prep time:   - Have adaptable and relevant case study-based examples, which can be slightly changed for each question. - Go into depth on the technology stack and processes - languages, frameworks, applications etc. - Painting a picture of how it comes together and your specific role. Always saying 'I', instead of 'we'. - Include all projects, outcomes, stakeholders, teams, and your role. Add ROI/stats where possible. - Use a structured answer highlighting the full end-to-end process (STAR, CARL) - this method will make your life easier.   During the interview:   - Think about the question and slow down. - Use a pre-prepared case study-based example you are passionate about, that answers the specific question. - Be clear and concise, making your relevant point early. - Have a flashcard that says STOP or PAUSE. To help make sure you don’t overtalk/ go off on a tangent. - Have fun explaining your projects and enjoy your interview! Data Engineering community... Use these points to support your interview prep and I hope it gets you closer to the job you want! #dataengineer #interview #interviewtips #hiring #dataengineering

Mastering the System Design Interview for Data Engineering Roles As data engineering continues to evolve, system design interviews have become an essential component of the hiring process. Despite its increasing popularity, there's still a lot of confusion surrounding this type of interview - both from candidates and companies alike. So, why has system design become so crucial in data engineering interviews? The answer lies in the rapid growth of big data systems and the proliferation of databases across various industries. With multiple data warehouses and complex architectures becoming the norm, it's essential for companies to assess a candidate's ability to design and optimize these systems. The system design interview is an interactive role-playing exercise that allows both parties to get to know each other better. To prepare effectively, it's crucial to: * Refresh your knowledge: Review the fundamentals of networking and system design from your past courses. * Reflect on past experiences: Think about how you've designed systems in the past and how you would improve them if given the chance. * Develop a visual communication strategy: Practice efficiently drawing out your solution using either virtual or physical whiteboards. By focusing on these areas, you'll be well-prepared to tackle the system design interview with confidence. Remember, this is an opportunity for both you and the company to learn more about each other's approaches to data engineering challenges. Share your thoughts and experiences with system design interviews in the comments below! #systemdesign #dataengineering #interviewing

Would you like to get free profile feedback for better results in interviews? Over the past 20 years I've interviewed hundreds of data practitioners for all roles and levels, in small and big companies. In the upcoming Data Engineering and Machine Learning summit 2024, organised by Benjamin Rogojan and Xinran Waibel, I'm planning a session on the "Behind the Scenes of the Data Interviews". In this session I'm planning to conduct live profile reviews for 1-2 people, and give them real-time feedback. The purpose of this is to explain the thinking process of interviewers/hiring managers when they review profiles. I'm looking for volunteers for this session. If you'd like to volunteer and get free feedback on your profile, please fill the form in the comments.

Communicating during interviews is one of the most critical factors which decides if you're going to make it or not You might come across variety of samples (recruiters) during interviews: 1. Ones who themselves communicate poorly - You dont understand what they want to ask - Their requirements are often incomplete Soln: Keep engaging with meaningful questions , " Just to clarify , You want me to answer logistic regression's model diagnsotics , is my understanding correct?" 2. Who have fragile egos - These people have less knowledge and high ego. - They love to crush you during interview , if you give them answer on architecture of Bert , they will ask you about roberta bert if you answer that as well then they will ask distilbert , albert and all brother sisters of bert, Even if you fail anywhere they will take a stand and with deep voice ask you to get your basics clear. Soln : Dont answer with a tone to fight , answer them politely , respect their opinions (even if they arent making sense) (i.e not buttering them) Eg: "" Yes Thats a valid & interesting point , although based on my experience /learning , I percieved this concept in this way. I will surely like to learn more about this"" 3. Specialist : - These people have themselves worked in a specific setup , For eg : Say your panelist has only worked on deployment Then even if you are being interviewed for a data science role He/she will ask you only about deployment . I understand that deployment could be one imp factor in data science but its not the entire data science. Soln : If you think that you are entering a wrong terrain then politely convey your limitations while highlighting your strengths. Try to see if you can change direction of interview. Eg: " Sir , currently my max exposure is with EDA , Dashboarding,ML. I am aware about deployment practices & learning more about it as its crucial skill" 4. Business person : - These people have more business accumen and less DS interest. - They will keep asking you to prove business impact of your solution (They arent wrong but data science role is about DS mechanisms + business impact & not only about business impact). Remember , as a data scientist you are responsible for producing scalable solutions which adds value to business. Soln : Try to formulate answers by keeping metrics like revenue , engagement , relevance in mind. Eg: " I would think that if we use this churn prediction model and are able to capture 75% of churns in top 3 deciles we would be able to increase our business by X$ by retaining customer's leading to long term growth" 5. Rare to find Interviewer: - These people will make you comfortable during interview, - will make sure that its an assessment and not a session for humiliating you. Soln : Thank God for getting such an experience , these are rare of the gems we have on this planet. #datascience #growth #interviewpreparation #machinelearning #interview

Happy Tuesday, everyone! I just wanted to share an article i read with my fellow job hunters. It is an article covering the various types of quetions one could get asked in a Data Science interview (along with sample answers/a guideline). Definitely worth the 23 minutes! Are there any interesting questions you've been asked at a job interview? https://lnkd.in/dXijeB2g #data #datascience #datascientist #jobhunting #womenintech #kmonyepele

How you articulate your responses can significantly impact your interview performance. It's a sentiment echoed by many aspiring data analysts who, despite answering questions accurately, find themselves facing rejection. Let's explore a pivotal example to understand why the manner in which you respond holds immense importance: Interviewer: "Can you elaborate on the concept of data partitioning in distributed systems?" Candidate A: "Certainly, data partitioning involves dividing data into smaller subsets to enhance storage and processing efficiency in distributed systems." Candidate B: "Data partitioning is a fundamental strategy in distributed systems, crucial for optimizing performance and scalability. In a recent project, our team implemented data partitioning to manage a substantial influx of user data within our cloud-based analytics platform. By partitioning data based on geographic regions, we successfully mitigated query latency and significantly enhanced overall system performance." As demonstrated, Candidate B not only elucidates the concept but also provides valuable context through a real-world scenario. This showcases practical experience and a deeper grasp of the subject matter, elevating their standing in the interview process. For those entering the field, here's a perspective from a fresher: "As a recent entrant into the realm of Data Engineering, I'm eager to delve into concepts like data partitioning in distributed systems. While my professional experience is nascent, I've acquired a robust understanding through academic coursework and personal projects. I firmly believe that data partitioning plays a pivotal role in optimizing system performance and scalability, and I'm enthusiastic about applying this knowledge in practical scenarios." Even without direct project involvement, expressing a clear understanding and eagerness to learn can leave a lasting impression on interviewers. In summary, it's not merely about providing answers; it's about articulating your knowledge and experiences effectively during interviews. Offering context, sharing tangible examples, and demonstrating enthusiasm can set you apart in the competitive landscape of Data Engineering. Continuously refine your interview responses to showcase both your expertise and passion for the role. Post Idea Credits: Munna Das #DataEngineering #InterviewSuccess #CareerGrowth

Data Engineering - 10 Managerial round Interview Questions 1- Can you discuss your approach to leading and managing a data engineering team? 2- How do you ensure alignment between data engineering projects and overall business objectives? 3- Describe a time when you had to make a difficult decision as a data engineering manager. How did you handle it? 4- What strategies do you employ to foster a culture of innovation and continuous improvement within your team? 5- Can you provide examples of how you prioritize projects and allocate resources effectively? 6- How do you handle conflicts or challenges within your data engineering team? 7- Discuss your experience in collaborating with other departments, such as data science or product development. 8- How do you measure the performance and success of your data engineering team? 9- Can you share a project where you successfully implemented data governance and compliance measures? 10- How do you stay updated with industry trends and best practices in data engineering leadership? How would you answer these? Let me know in the comments section. What other Interview questions have you faced in the managerial round? #dataengineering #interviews

🔍 Common Spark Interview Questions and Answers 🌟 Are you preparing for a Spark interview? Here are some key insights on transformations in Spark that could help you ace your next interview: 1. **Transformation in Spark:** It's an operation on an RDD that creates a new RDD without altering the input data. Remember, transformations are lazy and execute only when an action is triggered. 2. **Narrow vs. Wide Transformations:** Narrow transformations process data within the same partition efficiently, while wide transformations involve data shuffling across the network, making them more costly. 3. **Lazy Transformations in Spark:** These are designed to optimize the execution plan by building a lineage graph of transformations. This minimizes data passes and reduces unnecessary computations. 4. **Examples of Wide Transformations:** `groupByKey()`, `reduceByKey()`, and `join()` are some instances. They are expensive due to data shuffling across different nodes, incurring significant I/O and network overhead. 5. **Optimizing Wide Transformations:** Spark utilizes techniques like pipelining to execute multiple narrow transformations before wide ones. Additionally, Tungsten and Catalyst optimizers enhance performance. Understanding the nuances of transformations in Spark is crucial for job optimization, directly impacting performance outcomes. During interviews, articulating these concepts clearly showcases your expertise in distributed data processing within Spark. 💡 #DataProcessing #SparkInterview #TechInterview #DataEngineering #DistributedComputing #Spark

Here is a #1 secret to acing any Data Science Interview. I talked with countless ML engineers and Data Scientists from Big Tech about their successful interview learnings and here is the common tip I hear the most: "You need to act like a consultant. At the end of the day, you are providing a service to a company " Just like on a first discovery call, you need to see if you are a good fit for each other. You can do it only if you understand their current problem and their business well. Here is how you can do it: 1. Interviewer: Provides you with a context and a current business goal/problem. 2. You: Instead of answering right away, pause and ask clarifying questions. 3. Interviewer: Provides more context. 4. You: Repeat what you understood, pause, and think, and then respond. (Feel free to ask more clarifying questions if necessary) 5. Interviewer: Asks more follow-up questions. 6. You: Provide more value and ask them for feedback to adjust your answer further. ♻️Wash. Rinse. Repeat. If you want to outcompete other applicants, act like a salesman, and show your genuine interest and true competence to win their trust. Good luck! #mlengineer #interview #bid

Technical skills will secure you an interview, soft skills will help you secure the job 🏆 My days are spent speaking to hiring managers & data professionals, and there is one consistent theme I hear over and over again - soft skills are just as important as technical expertise Hiring managers are looking for Data professionals who can communicate clearly, collaborate with cross-functional areas of the business, and adapt to fast-paced evolving environments 📃 Here are some soft skills that you should consider & be prepared to evidence in your next Data interview: 🎯 Communication When discussing previous examples of work, demonstrate your ability to communicate complex technical concepts to non-technical stakeholders. Can you explain your data process in simple terms? Can you bring the numbers to life? Can you deliver an engaging Data Story? 🎯 Collaboration Working with cross-functional teams in the business is central to the majority of Data projects. Showcase your ability to embed yourself into different business areas, communicate data clearly to non-tech stakeholders, and solve problems as a team. Highlight real-life examples of how you've worked with others & emphasize the part you had to play in working together as a team. 🎯 Problem Solving No two projects or problems you encounter will be the same, each will present a new problem to be solved. Evidence times that you have comfortably adapted to new tools, techniques or a last-minute change in business need or strategic direction of the project. What soft skills would you say are most important to showcase during a Data interview?