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We are seeking a highly skilled Data Engineer with expertise in Python, PySpark, SQL, Azure, and Microsoft Fabric. The successful candidate will be responsible for designing, developing, and maintaining scalable data pipelines and data architectures across cloud-based platforms. Responsibilities Collaborate with cross-functional teams to design, develop, and implement scalable data solutions using Python, PySpark, SQL, and Azure services.Design, develop, and maintain robust ETL/ELT pipelines for efficient data ingestion, transformation, and integration.Leverage Azure Data Factory, Databricks, Microsoft Fabric to build and maintain modern data architectures such as medallion architecture.Work closely with Power BI developers and data analysts to enable seamless data integration for reporting and analytics.Participate in all phases of the Software Development Lifecycle (SDLC) including requirement gathering, development, testing, and deployment. Write efficient, scalable, and optimized code to handle large volumes of structured and unstructured data.Ensure data quality, consistency, and reliability across data pipelines and storage systems.Collaborate with data scientists, analysts, and product teams to solve business and technical challenges.Conduct code reviews and provide constructive feedback to team members.Troubleshoot, debug, and resolve data pipeline and performance issues.Stay up to date with industry trends and best practices in data engineering and cloud technologies. Requirements Strong experience in data engineering using Python, PySpark, and SQL.Proven expertise in designing and implementing ETL/ELT pipelines for data ingestion, transformation, and integration.Solid understanding of relational databases with proficiency in SQL, including creating views and stored procedures.Hands-on experience with Azure services such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics. Experience implementing medallion architecture (Bronze, Silver, Gold layers) using Delta Lake in Azure Data Lake Storage (ADLS) or OneLake. Familiarity with Microsoft Fabric for unified data integration, processing, and reporting. Strong experience working with large-scale datasets and distributed computing frameworks like Spark.Familiarity with version control systems such as Git.Understanding of software development best practices and agile methodologies.Strong problem-solving and analytical skills.Excellent communication and collaboration skills.Good to have knowledge of GenAI concepts including RAG architecture, AI agents, SKILL.md workflows, and AI orchestration frameworks. .
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.