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Heads up, grads and students – Visa’s looking to bring some new data engineers on board. They’re holding a recruitment drive off campus for the gig. Give the deets below a peek if you wanna throw your hat in the ring.
Job Description :-
| Company Name | Visa |
| Job Role | Data Engineer |
| Qualification | Any Degree |
| Experience | 0-1 years |
| Batch | Any Batch |
| Location | Bengaluru, Karnataka, India |
| CTC/Salary | Not Disclosed |
Tips to Prepare an ATS-Friendly Resume :-
1. Use Keywords Put in words like “ETL,” “data pipelines,” “data warehousing,” “Hadoop,” “Spark,” “AWS,” “SQL,” “Python,” and “Kafka.” Check the job description for other good skills and tools to mention. 2. Show Your Tech Skills Have a "Skills" section to show you know key data engineering tools and computer languages that are needed (e.g. SQL, Python, Scala, Hive, Redshift, Snowflake, etc.). List cloud platforms (AWS, GCP, Azure) and big data frameworks (Hadoop, Spark). 3. Talk Up Projects and Achievements In the "Experience" part, focus on data engineering projects you did. Explain how you built or improved data pipelines, handled lots of data, or designed data systems. Use numbers to show achievements (e.g. “Built data pipelines that processed 2 TB of data daily”). 4. Make it Simple and Clean Use a basic format with clear sections like “Skills,” “Experience,” “Education,” and “Certifications.” Don't use anything too fancy that might confuse computer programs reading it. 5. Show Your Impact List achievements with numbers, like “Made data pipelines 40% faster” or “Made a data warehouse that improved speed of queries by 30%.” 6. List Certifications Mention any cloud or data certifications (AWS, Google Cloud, Microsoft Azure). 7. Use Power Words Start bullet points with action verbs like “Developed,” “Engineered,” “Optimized,” “Designed,” and “Deployed” to highlight your work. 8. Pick the Right File Type Save as a .docx or .pdf so resume reader programs can easily understand everything.
Tips for acing the Data Engineer job interview:-
*Do talk about your chops with tools like Hadoop, Spark, Kafka, SQL, Python, AWS and all that. Give real examples of using them to wrangle huge amounts of data.* *Do chat about how you've fixed problems like designing more efficient data pipelines. Companies want to know you can handle whatever tech challenges get tossed your way.* *Expect you'll have to write some code with SQL queries, Python data transformations - practice that stuff!* *Show you understand distributed computing, batch vs stream processing, how to use big data. Know your stuff in that area.* *Ask good questions about their data systems, architecture, challenges - it shows you really care.* *But don't just talk tech! Show how you play nice on cross-functional teams too.* *Don't dance around optimization techniques and performance tuning. How have you actually improved speed and cost before?* *You'll probably need to talk cloud platforms like AWS, Azure, GCP. Brush up if you're rusty!* *Oh and don't forget those soft skills even if you know your technical stuff cold! Working well under pressure matters.* *Get specific about your experience - no vague answers. Talk details on your roles, challenges overcome, results delivered.
Eligibility / Qualification:-
- – A college degree in Computer Science or something similar
- – At least 1 year of experience building software focused on data, using standard development methods
- – To be really good with data structures, algorithms, and solving tough problems
- – Know-how with Big Data technologies like Hadoop, Java, Spark, Kafka, and Hive
- – Understand Unix/Linux and know SQL
- – Hopefully understand data visualization tools like Tableau
- – Be familiar with basic software design
- – Have worked with Agile
- – It’s a big plus if you know how to develop APIs
- – Be a strategic thinker who gets business needs of clients
- – Be careful with details but also see the big picture
- – Play nice with others and be flexible
Roles And Responsibilities:-
- – Make high-quality stuff and features
- – from the first idea to getting it out the door. This means coming up with ideas, building things, testing, and making sure what gets released keeps working and folks can get help if they need it.
- – Understand everything about the product. Make sure your part gets done on time when working with the team.
- – Figure out how much effort things will take, spot risks, and be straight with the team and higher-ups about trade-offs.
- – Work together on keeping things integrated and testing stuff. Chip in on core product code too.
- – Keep the team up to date on how things are going, issues that come up, and possible risks. Do this fast and accurately.
- – Sometimes help out with data, infrastructure, and other systems as needed.
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