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We are seeking an experienced and driven Data Engineer with 5+ years of hands-on experience in building scalable data infrastructure and systems. You will play a key role in designing and developing robust, high-performance ETL pipelines and managing large-scale datasets to support critical business functions. This role requires deep technical expertise, strong problem-solving skills, and the ability to thrive in a fast-paced, evolving environment. Key Responsibilities : Design, develop, and maintain scalable and reliable ETL/ELT pipelines for processing large volumes of data (terabytes and beyond). Model and structure data for performance, scalability, and usability. Work with cloud infrastructure (preferably Azure) to build and optimize data workflows. Leverage distributed computing frameworks like Apache Spark and Hadoop for large-scale data processing. Build and manage data lake/lakehouse architectures in alignment with best practices. Optimize ETL performance and manage cost-effective data operations. Collaborate closely with cross-functional teams including data science, analytics, and software engineering. Ensure data quality, integrity, and security across all stages of the data lifecycle. Required Skills & Qualifications : 7 to 10 years of relevant experience in bigdata engineering. Advanced proficiency in Python. Strong skills in SQL for complex data manipulation and analysis. Hands-on experience with Apache Spark, Hadoop, or similar distributed systems. Proven track record of handling large-scale datasets (TBs) in production environments. Cloud development experience with Azure (preferred), AWS, or GCP. Solid understanding of data lake and data lakehouse architectures. Expertise in ETL performance tuning and cost optimization techniques. Knowledge of data structures, algorithms, and modern software engineering practices. Soft Skills : Strong communication skills with the ability to explain complex technical concepts clearly and concisely. Self-starter who learns quickly and takes ownership. High attention to detail with a strong sense of data quality and reliability. Comfortable working in an agile, fast-changing environment with incomplete requirements. Preferred Qualifications : Experience with tools like Apache Airflow, Azure Data Factory, or similar. Familiarity with CI/CD and DevOps in the context of data engineering. Knowledge of data governance, cataloging, and access control principles. Skills : Python,Sql,Aws,Azure, Hadoop .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.