Job Description

As a Data Engineer, you will be responsible for managing and optimizing data pipelines, implementing ETL processes, and ensuring data is clean, accurate, and available for analysis. You will work with cross-functional teams to deliver high-quality data infrastructure and analytics solutions. Responsibilities: Design, implement, and manage ETL pipelines to ingest and process data from multiple sources. Develop and maintain data warehouses and data lakes for efficient storage and querying. Write optimized SQL queries for data extraction, transformation, and analysis. Collaborate with Data Analysts, Data Scientists, and other stakeholders to ensure data availability and usability. Perform data quality checks and troubleshooting to ensure high-quality, reliable data. Work with cloud platforms such as AWS, Azure, or GCP to deploy and maintain data solutions. Automate and monitor ETL workflows for maximum efficiency and scalability. Assist in the development and optimization of data models and database architectures. Ensure compliance with data governance and security best practices. Stay up-to-date with the latest trends in data engineering, cloud technologies, and big data. Required Skills & Qualifications: 4 to 6 years of experience in Data Engineering or a related field. Strong proficiency in Python for data manipulation and automation. Hands-on experience with SQL for querying, transforming, and analyzing data. Solid experience with ETL processes and tools (eg, Apache Airflow, Talend, Informatica). Experience with cloud platforms such as AWS (eg, Redshift, S3, Lambda) or Azure (eg, Azure Data Factory, Azure SQL Database). Strong understanding of data warehousing and big data concepts. Familiarity with version control systems like Git. Good understanding of data modeling and database design principles. Ability to work independently and in a team-oriented, collaborative environment. Strong problem-solving skills with the ability to troubleshoot and resolve data issues.