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Dear Candidate, Please find the detailed JD as - Role: Senior Data Engineer Key Responsibilities & Expectations Design, develop, and support scalable data engineering solutions with strong focus on automation, data quality, scripting, and production readiness. Apply proper AI knowledge to understand modern AI models and AI-assisted development tools, including prompts, tokens, context windows, reasoning behaviour, model responses, and practical integration points within data engineering workflows. Develop and maintain Python coding and scripting solutions for automation, data validation, file processing, and reusable engineering utilities. Drive automation initiatives to reduce manual effort, improve repeatability, strengthen controls, and accelerate data processing workflows. Perform Unix-based scripting, scheduling support, file handling, log analysis, and operational troubleshooting across data engineering environments. Contribute to Spec Kit development by building, enhancing, validating, and maintaining specification-driven data processing components. Ensure strong data quality through validation rules, reconciliation checks, exception handling, root cause analysis, and continuous monitoring. Must-Have Skills & Experience 7-8 years of experience as a Senior Data Engineer or Data Engineer in technical, data-driven environments. Strong technical orientation with hands-on understanding of complex systems, data flows, batch processes, pipelines, and integration patterns. Proper AI model and AI-assisted coding knowledge with ability to understand how large language models, including prompts, tokens, context windows, reasoning modes, code suggestions, model limitations, and practical data engineering use cases. Strong Python coding and scripting experience for automation, data processing, data quality checks, file handling, reusable utilities, and operational support. GitHub Copilot exposure for AI-assisted Python coding, script generation, code review support, debugging help, unit test creation, documentation, and developer productivity improvement. Hands-on Unix experience including shell scripting, command-line operations, file transfers, scheduling support, log review, and troubleshooting. Proven hands-on exposure to data quality concepts, validation frameworks, reconciliation processes, exception handling, and defect prevention practices. AI model and GitHub Copilot awareness, including how prompts are interpreted, how context windows affect outputs, how responses and code suggestions are generated, and how data quality impacts AI-assisted automation. Automation-focused engineering mindset with experience creating scripts, reusable components, monitoring checks, and repeatable operational processes. Experience in Spec Kit development, including specification interpretation, component build, testing, validation, and maintenance of data processing logic. DevOps awareness with exposure to version control, CI/CD concepts, deployment readiness, environment configuration, monitoring, and production support practices. Excellent written and verbal communication skills, with strong documentation capabilities. Nice-to-Have Experience with advanced automation frameworks, reusable script libraries, or AI-enabled data engineering accelerators. Healthcare domain knowledge Advanced Python experience for integrating AI model outputs into automation workflows, building data profiling utilities, and developing production-grade scripts. AI DLC exposure, including awareness of AI development lifecycle practices such as prompt design, model selection, output validation, testing, monitoring, governance, documentation, and responsible AI usage. Regards, Ketki .
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.