Data Engineer

Key Responsibilities:
Data Pipeline Development: Design, develop, and maintain robust data pipelines to collect, store, and process large volumes of data from multiple sources, ensuring data accuracy and integrity.
Data Integration: Integrate data from various sources, including internal databases, external APIs, and third-party services, to create unified datasets for analysis and reporting.
Database Management: Manage and optimize large databases (e.g., SQL, NoSQL), ensuring high performance, scalability, and security. Handle data modeling and schema design.
ETL Processes: Build, maintain, and optimize ETL (Extract, Transform, Load) processes for efficient data extraction, transformation, and loading into data warehouses or data lakes.
Data Quality Assurance: Ensure the accuracy, consistency, and quality of data by performing data validation and implementing automated data cleaning processes.
Collaboration with Data Scientists/Analysts: Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver clean, well-structured data for analysis.
Performance Optimization: Continuously monitor and optimize data pipelines, databases, and queries to ensure optimal performance, scalability, and cost efficiency.
Cloud Technologies: Utilize cloud platforms (e.g., AWS, Azure, Google Cloud) and services (e.g., S3, Redshift, BigQuery) for scalable data storage, processing, and analytics solutions.
Data Security and Compliance: Ensure that all data storage, processing, and sharing practices comply with organizational and regulatory standards for data privacy and security.