Lead Data Engineer

You should have 6-8 years of experience with a deep understanding of the Spark framework, along with hands-on experience in Spark SQL and Pyspark. Your expertise should include Python programming and familiarity with common Python libraries. Strong analytical skills are essential, especially in database management, including writing complex queries, query optimization, debugging, user-defined functions, views, and indexes. Your problem-solving abilities will be crucial in designing, implementing, and maintaining efficient data models and pipelines. Experience with Big Data technologies is a must, while familiarity with any ETL tool would be advantageous. As part of your responsibilities, you will be working on projects to deliver, review, and design PySpark and Spark SQL-based data engineering analytics solutions. Your tasks will involve writing clean, efficient, reusable, testable, and scalable Python logic for analytical solutions. Emphasis will be on building solutions for data cleaning, data scraping, and exploratory data analysis, ensuring compatibility with any BI tool. Collaboration with Data Analysts/BI developers to provide clean and processed data will be essential. You will design data processing pipelines using ETL techniques, develop and deliver complex requirements to achieve business goals, and work with unstructured, structured, and semi-structured data and their respective databases. Effective coordination with internal engineering and development teams to understand requirements and develop solutions is critical. Communication with stakeholders to grasp business logic and provide optimal data engineering solutions will also be part of your role. It is important to adhere to best coding practices and standards throughout your work.,