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Tier-1 employer, general Data Engineer title, mid experience range, metro location, broad technical requirements.
Data engineering skills are transferable but industrial IoT and Azure/Databricks focus increases domain specificity.
Explicit 3–8 years plus mandatory Python, Azure and Databricks experience increases strictness.
Design and implement scalable industrial IoT product features and data-processing algorithms using Python.
Develop backend services, REST APIs, data pipelines, and ETL processes for large-scale timeseries datasets.
Deploy and operationalize solutions on Microsoft Azure; maintain code quality with testing and resolve production issues.
Bachelor’s or master’s degree in Computer Science, Electronics and Communication, Electrical Engineering, Data Engineering, or equivalent.
3-8 years of software development experience, including at least 2 years of production-grade Python development.
Experience with Python frameworks (FastAPI or Flask), timeseries data analytics (Pandas, NumPy, Scikit-learn), and Microsoft Azure services (Azure Functions, Azure Databricks).
Proficiency in software engineering best practices including OOP, design patterns, SOLID principles, testing (PyTest), Git, CI/CD, and DevOps.
Experienced in building scalable backend and data pipeline solutions within an agile, multidisciplinary development team.
Strong background in industrial or operational analytics with timeseries datasets and machine learning techniques.
Demonstrates software architecture expertise and ability to operationalize applications on cloud platforms, primarily Microsoft Azure.