Data Engineer

Technical Skills Required:
ETL Concepts:Strong understanding of Extract, Transform, Load (ETL) processes.Ability to design, develop, and maintain robust ETL pipelines.Database Fundamentals:Proficiency in working with relational databases (e.g., MySQL, PostgreSQL, Oracle, or MS SQL Server).Knowledge of database design and optimization techniques.Basic Data Visualization:Ability to create simple dashboards or reports using visualization tools (e.g., Tableau, Power BI, or similar).Query Optimization:Expertise in writing efficient, optimized queries to handle large datasets.Testing and Documentation:Experience in validating data accuracy and integrity through rigorous testing.Ability to document data workflows, processes, and technical specifications clearly.Key Responsibilities:Data Engineering Tasks:Design, develop, and implement scalable data pipelines to support business needs.Ensure data quality and integrity through testing and monitoring.Optimize ETL processes for performance and reliability.Database Management:Manage and maintain databases, ensuring high availability and security.Troubleshoot database-related issues and optimize performance.Collaboration:Work closely with data analysts, data scientists, and other stakeholders to understand and deliver on data requirements.Provide support for data-related technical issues and propose solutions.Documentation and Reporting:Create and maintain comprehensive documentation for data workflows and technical processes.Develop simple reports or dashboards to visualize key metrics and trends.Learning and Adapting:Stay updated with new tools, technologies, and methodologies in data engineering.Adapt quickly to new challenges and project requirements.Additional Requirements:Strong communication skills, both written and verbal.Analytical mindset with the ability to solve complex data problems.Quick learner and willingness to adopt new tools and technologies as needed.Flexibility to work in shifts, if required.Preferred Skills (Not Mandatory):Experience with cloud platforms (e.g., AWS, Azure, or GCP).Familiarity with big data technologies such as Hadoop or Spark.Basic understanding of machine learning concepts and data science workflows.