Role: Senior Data Engineer
Location: Mumbai
Years of experience: 9+ years
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 behavior, 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 accelerator 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.
