Job Description

Our Team What You Will Do Design, develop, enhance, and debug the existing SQL code. Implement best practices for high data availability, computational efficiency, cost-effectiveness, and data quality within Snowflake and AWS environments. Build and enhance environments, processes, functionalities, and tools to streamline all stages of data lake implementation and analytics solution development, including proof of concepts, prototypes, and production systems. Drive automation initiatives throughout the data lifecycle based on configuration-driven approaches, ensuring repeatability and scalability of processes. Stay updated with relevant technology trends and product updates, especially within AWS services, and incorporate them into the data engineering ecosystem. Collaborate with cross-functional teams following agile methodologies to ensure alignment with business objectives and best practices for data governance. Who You Are (Basic Qualifications) 8-10 years of professional experience in data engineering or Big data and data warehousing. Strong hands-on SQL and Data Modelling skills (must) and intermediate Python. Proficient in handling high volume of data and developing SQL queries which are scalable and performant. Proven experience in ETL/ELT concepts and methodologies. Proficiency in data analytics tasks such as data wrangling, mining, integration, analysis, visualization, data modelling, and reporting, using BI tools. Expertise in primary skills including Data warehousing, SQL. Understanding and proven hands-on experience of S3, step functions, LAMBDA, SNOWFLAKE, step-functions. Excellent communication skills with the ability to effectively communicate complex technical concepts and drive initiatives within the team and across departments. Proven track record of contribution-driven work ethic, striving for excellence in every aspect of data engineering and software development. Have delivered BI project by working on any modern BI tools like Power BI, Tableau, Qlik Sense. Knowledge or hands on experience of Qlik Replicate, DBT and Denodo for data virtualization layer