How do you build a strong data engineering and science resume?

To build a strong data engineering and science resume: Skills: Highlight proficiency in Python, SQL, Java, Scala, AWS, Azure, GCP, Airflow, Spark, MapReduce, Kafka, Redshift, and Snowflake. Projects: Emphasize relevant projects with quantifiable impact. E.g., "Reduced data processing time by 10% through real-time data pipeline development using Apache Spark." Tailoring: Customize your resume for each job application by focusing on skills and experiences most relevant to the specific role. Proofread: Carefully review for typos and grammatical errors to maintain professionalism. Keep resume concise and informative to stand out to potential employers.

1.Summary/Objective: Clearly state your expertise and career aspirations in data engineering or data science. 2.Skills: Highlight key technical skills such as Python, R, SQL, Hadoop, Spark, TensorFlow, SQL, and NoSQL databases. 3.Experience: Detail data engineering or data science projects with action verbs and measurable achievements. 4.Education: List degrees, institutions, and relevant coursework/projects. 5.Projects: Showcase impactful data projects, specifying your role, technologies used, and outcomes. 6.Certifications/Training: Include relevant certifications and specialized training. 7.Tailoring: Customize each resume by integrating keywords from the job description.

Although it's a good idea to tailor your skills based on the job description, one thing to be mindful about is that you don't end up putting everything in the job description on your resume. While it might get you through the initial screening, if you end up lying on your resume by adding skills you have never used in the past, it's pretty easy for recruiters to catch on to it during the interviews. Be true to yourself and stay confident in your skills.

Furthermore, while learning languages, algorithms, and techniques will help you to land a job and start working, having a strong understanding of the underlying systems and networking that happens can allow you to find innovative solutions that will help you stand out and showcase your skills.

Definately one is supposed to highlight skills in each role and that leads to important point that one should highlight trending tech if they have worked on it. For Data Engineers, hands on experience in Spark batch, Spark streaming and Data Mesh makes profile trendy and eye catchy whereas for Data Scientist, reading data from hadoop system and training/testing model on cloud using Spark is in demand. So highlighting trending tech stack becomes important along with mandatory skill set.

From data ingestion to data model long, visualisation, quality check and production deployment every phase of the project for a data engineer plays a crucial role. Building an end to end production deployed pipeline which quite scalable and robust helps you define your project more confidently. It also gives you an opportunity to accept the difficult and challenging conditions arrived during building a pipeline along with your skills and experience. This helps to showcase your ability to solve critical problems and showcase the relevance during interviews. Hence, mentioning your projects with great statistics achieved and tasks accomplished in clear and concise manner helps to land a competitive advantage in the job market.

Have projects deployed and easily accessible. Have links of your top projects and a general link to all of your others. Make sure to write good documentation that explains your projects, how you solved the problem, what the project achieves and most importantly what you learnt from completing the project. Highlighting where you got stuck and how you managed to solve issues is also good to document as it highlights a skill you will be using in the real world. It shows you can problem solve, think deeply, research and implement.

One of the most compelling projects for a data engineer to showcase their skills is to build an end-to-end ETL pipeline. When you work on such a project, you'd be working with a bunch of tools and technologies like - programming language (like python), databases(like MySQL), cloud services (like S3, Glue, Athena, etc. if your preferred cloud is AWS), Terraform (if you wanna go a step further and have your cloud configurations to be repeatable and reproducible), Git, Docker, CI/CD to ensure good deployment practises. This gives you something solid to work on and talk about during interviews, especially if you're a new grad. It also gives the recruiter a path to ask questions and learn more about your skills.

One thing I have found helpful when showcasing your projects is not to just focus on the project tasks themselves, but to demonstrate your deep conceptual knowledge in solving common issues for the industry and the value this has. This aspect of the CV should not be a mere description but a short lesson for the person reading; this will spark curiosity that you can capitalize on during the interview by going into more depth on the topic.

In my view, it is important to structure this part in a business application point of view. Instead of describing you used these tools and did this, it is important to explain the problem you solved in the project and the impact. This will be especially helpful to get through the initial CV screening done by nontechnical recruiters. Most importantly this help you to build the “story you want to share”.

Quantifying data engineering achievements should encompass more than just time metrics; it should also encompass aspects like data quality and pipeline uptime, which are often more crucial than achieving significant speed improvements.

In order to quantify your achievements, a great formula to use is "Accomplished [X] as measured by [Y] by doing [Z]" - Laszlo Bock. Also, make sure to avoid using weak action verbs and repeating the same action verb multiple times. Use strong action verbs and in a way to highlight a wide range of soft skills as well. You can search online for which action verb corresponds to which soft skill. For more details, do checkout resumeworded.com and try to scan your CV using this site to check for ATS score and more specific details to make your CV ATS friendly.

List how you have made impact in your roles. For example instead of saying I worked on Data Ingestion or a credit scoring model. Elaborate and add details of the impact. For example at Company X I worked on Data Ingestion, adding 10 new data sources and X amount of data. These new datasources resulted in the renewal of our three biggest contracts and 2 new customers that generated X amount of revenue. Showing you have an understanding of your impact in terms of the overall business will put you in good stead. This shows you understand the bigger picture, are able to network outside of the department and know how your goals and work roll up to overall company success.

Both Data Engineering and Data Science domain has lot of room for enhancements. Data Engineers must show how optimization technique like partition and clustering/data sharding and split along with Spark processing time reduction would have bring efficiency since cost, CPU and time is always at the crux. At the same time Data Scientist can share how their models brought efficiency to the business and if they tried and tested a specific model with regularization was time and cost effective along with best accuracy and F1 for respective domain and particular type of dataset which showcase R&D mindset.

For Data Engineering, make sure you adequately highlight the core skills- - Cloud Platform (AWS/AZURE/GCP) - Distributed tech like Spark - Languages SQL/Python - Fundamentals of Data Warehousing and Data Modeling

Whether it is a Data Engineer or a Data Scientist or any other tech stack, since ATS(Applicant Tracking System) software are being relied upon to filter a profile more efficiently, one should try to leverage suggestions provided by word document, LinkedIn experts and Mentors to format and create resume which is both human and software friendly. That includes keeping keywords as heading under appropriate section and remove redundancies. Portals like Workday are great place to test how your Resume is skimmed/read through sofware.

Resumes should be in simple and at the same time should highlight the impact you have created in your past data engineering projects. Highlight how you accomplished critical and complex parts of your data engineering projects whether its managing massive pipelines, cloud cost optimization or monitoring and scheduling. Please don't forget to add data engineering related keywords more often to resume as resumes are being filtered through software's.

There are many tools available that can assist you in making a good resume. Look for some examples online. See which sections should be prioritized and come first based on whether you have experience or are fresh out of university. After checking for some examples, look for some templates that you can use if you are struggling to come up with a format. For making sure your header and experience descriptions are clear, use ChatGPT to help you reword paragraphs in different tones or to highlight and emphasize certain points. Finally have a friend or family member read over your CV to check if it is clear to them. The clearer your CV to someone outside of the field, the better. You want to make your achievements and abilities easily understood

Opting for a simple layout is often the most effective choice; overly complex designs can distract from your resume's content and message.

That’s true ! But don’t neglect to write a small paragraph about yourself . Who are you , what do you look for , what are you interested in, what is your added value for the position you apply for ?

Make sure to keep your resume up to date with your latest positions and achievements. Your resume is an opportunity to sell your self and highlight your abilities. When doing so it is important that you highlight all of your abilities, experiences and achievements. If you find your resume becoming very long then think about the type of job you are applying for, elaborate on the experiences and achievements that are most relevant and be brief on others that you have done but are not as relevant to your future applications.

If your company has adopted geospatial services to process and feed data into BigQuery or have started leveraging Redshift from AWS, then it means you have are working on emerging tech. Similarly, if you have prepared a model using spark mllib or created a chatbot using LLM like BERT or BART, then you are trying to leverage models which has been recently open sourced. Thus a Data Engineer or Data Scientist must stay in touch with posts from Medium or company announcements to remain up to date which will help keep resume upto date.

Your resume should demonstrate that you've consistently picked up new tools and technologies through your projects, showcasing your commitment to staying up-to-date in the rapidly evolving realm of data engineering and science.

Consider letting someone else read over your CV/resume. Get input and make adjustments accordingly. When you apply for jobs and are unsuccessful this a good opportunity to ask for what would have been things on your CV that could have stood out and help guide you towards future learning endeavors, up-skilling and networking. Use every opportunity you have for feedback that can guide you to where you want to be.