Remote Data Engineer

A Remote Data Engineer is responsible for designing developing building and maintaining scalable data pipelines and infrastructure that enable organizations to collect process store and analyze large volumes of data. The role involves developing data integration solutions optimizing database performance implementing data transformation processes and ensuring data quality reliability and security. Working remotely the Data Engineer collaborates with data analysts data scientists software developers and business stakeholders to deliver reliable data solutions that support business intelligence reporting analytics and strategic decision-making. Key Responsibilities Design develop and maintain scalable data pipelines for collecting processing transforming and storing structured and unstructured and optimize Extract Transform Load (ETL) and Extract Load Transform (ELT) data integration solutions to consolidate information from multiple databases applications APIs and external data implement and maintain data warehouses data lakes and data lakehouse efficient SQL queries and develop database procedures to support data processing and reporting and maintain data workflows using orchestration tools such as Apache Airflow or similar data transformation and processing solutions using Python SQL and other relevant programming data pipelines and troubleshoot failures performance bottlenecks and data quality data validation cleansing deduplication and transformation to maintain data accuracy and with data analysts and data scientists to prepare reliable datasets for business intelligence machine learning and advanced database schemas queries storage systems and processing jobs for performance and cost data security measures access controls encryption and governance practices to protect sensitive automated data quality checks monitoring systems and error-handling technical documentation for data architectures pipelines schemas and transformation cloud-based data engineering solutions using platforms such as AWS Microsoft Azure or Google version control automated testing and CI/CD practices for data engineering with cross-functional teams to understand business requirements and translate them into reliable technical opportunities to improve data processing efficiency scalability reliability and operational performance.