Data Engineer

Role & responsibilities Mandatory Skill: Programming Languages: Proficient in Python , PyTest and PySpark, SQL with a strong understanding software engineering best practices. Oops Concept: Strong Knowledge of It. Cloud Computing: Utilize Azure cloud based data platforms, specifically leveraging Databricks and Delta Live Tables for data engineering tasks, while effectively utilizing services related to storage, compute, and security. Data Pipelines: Design, build, and maintain robust and scalable and automated data pipelines for batch and streaming data ingestion of data and processing (Data bricks workflow). Data Architecture and Modeling: Design and implement robust data models and architectures that align with business requirements and support efficient data processing, analysis, and reporting. Orchestration: Utilize workflow orchestration tools to automate data pipeline execution and dependency management. Monitoring and Alerting: Integrate monitoring and alerting mechanisms to track pipeline health, identify performance bottlenecks, and proactively address issues. Strong Agile principles: Utilize Agile development methodologies, actively participating in sprint planning, daily stand ups, sprint reviews, and retrospectives. Be flexible and adaptable to changing requirements and priorities throughout the project lifecycle. Unity Catalog Good to Have: Github Actiom Datagog exposure Data Quality: Implement data quality checks and balances throughout the data pipeline, including profiling, validation, and root cause analysis, to ensure data accuracy, completeness, and consistency. CI/CD: Implement continuous integration and continuous delivery (CI/CD) practices for automated testing and deployment of data pipelines.