Data Engineer

Job Description: Nawy Proptech is in search of a dedicated and skilful Data Engineer to become a part of our lively team. As a Data Engineer, your pivotal role will involve handling our data management and analysis strategies. You will collaborate closely with our data scientists, analysts, and software engineers in order to plan, generate, and maintain data pipelines, ensuring that our data is readily available and reliable for analysis and making business decisions. Responsibilities: 1. Data Pipeline Development: Devise, build and sustain sturdy, scalable data pipelines for the ingestion, processing, and transformation of data from a variety of sources, which includes internal databases and external APIs. 2. Data Integration: Work alongside cross-functional teams to meld data from disparate systems and sources, ensuring accurate and consistent data. 3. Data Quality Assurance: Carry out data validation and quality checks meant to spot and rectify any problems in data pipelines, thereby upholding data integrity. 4. ETL Processes: Construct and streamline ETL (Extract, Transform, Load) processes to guarantee the timely delivery of data, supporting business intelligence and data analysis requirements. 5. Data Modeling: Lend a hand in data modeling strategies, which would include the design of data structures and schemas to fulfill analytics and reporting requirements. 6. Performance Optimization: Monitor and enhance the performance and efficiency of data pipelines and databases, making necessary adjustments to comply with SLAs. 7. Data Security: Implement data security measures and access controls in order to safeguard sensitive data and ensure adherence to data privacy laws. 8. Documentation: Keep thorough documentation for data pipelines, procedures, and systems for the purpose of knowledge exchange and future reference. 9. Troubleshooting and Support: Offer necessary support related to data-related issues and incidents, find out the root causes and implement solutions promptly. 10. Collaboration: Co-ordinate closely with data scientists and analysts to comprehend their data requirements and provide them with appropriate data infrastructure and tools needed. Requirements - Familiarity with cloud-based data platforms (for example, AWS, Azure, GCP). - An understanding of data orchestration tools (for instance, Apache Airflow, dbt). - Knowledge of containerization and orchestration (like Docker, Kubernetes). - Understanding of data security and compliance best practices.