Data Engineer

Data Engineer job description
Job title: Data EngineerJob summaryA Data Engineer is an IT professional who designs, builds, and maintains the systems and infrastructure for collecting, storing, and processing large-scale data. They are responsible for building robust data pipelines that clean, transform, and aggregate raw data, making it reliable, accessible, and optimized for data scientists, analysts, and other stakeholders. This role sits at the foundation of an organization's data ecosystem, ensuring that high-quality data is available to inform business intelligence, reporting, and machine learning initiatives. Key responsibilitiesDesign and maintain data architecture: Develop and manage scalable data architectures, including data warehouses, data lakes, and database systems (SQL and NoSQL), to meet business requirements.Develop and optimize data pipelines: Build, test, and maintain Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) processes to move data from various sources into destination systems.Manage big data technologies: Implement and manage distributed systems and big data frameworks like Apache Spark, Hadoop, and Kafka to process large, complex datasets efficiently.Ensure data quality and reliability: Implement data validation methods, error checks, and cleaning processes to ensure that data is accurate, consistent, and usable for downstream applications.Collaborate with data teams: Work closely with data scientists, data analysts, and other stakeholders to understand data needs, deliver data solutions, and support analytics projects.Implement cloud-native data solutions: Leverage cloud platforms like AWS, Azure, or Google Cloud and their respective data services (e.g., Redshift, BigQuery, Snowflake, Azure Data Factory) to build scalable and cost-effective data solutions.Monitor and optimize performance: Continuously monitor data pipelines and database performance, troubleshooting issues and optimizing workflows for speed and efficiency.Ensure data security and governance: Implement data security protocols, manage access controls, and ensure compliance with relevant data privacy regulations (e.g., GDPR).Document processes: Maintain clear and comprehensive documentation of data workflows, schemas, and infrastructure to facilitate knowledge sharing and maintain best practices.

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