Data Engineer

Experience in SQL Snowflake Creation Data Warehouse Exposure to connect to different data sources and readying data layers for business consumption ETL Snowflake, with extensive experience in data warehouse architecture, management and Snow pipe. User Defined Function vs Stored Procedure, Multi Clusters DWH, Views vs Secure Views & Materialized views in Snowflake. Tuning and how to improve performance in Snowflake. SQL and database development, with the ability to write complex queries and optimize database performance. Programming skills in Python, including experience with data libraries and frameworks. Ability to use varied techniques in data ingestion like unstructured and structured data with API, ODBC etc. Ability to architect ETL flows for ingestion. Understanding of data modeling principles and best practices. Implement robust, scalable data pipelines and architectures in Snowflake, optimizing data flow and collection for cross functional teams. Complex SQL queries and scripts to support data transformation, aggregation, and analysis. Python for automating data processes, integrating data systems, and building advanced analytics models, data modeling initiatives, ensuring the integrity and efficiency of data structures. With cross functional teams to understand data needs, gather requirements, and deliver data driven solutions that align with business goals. Data security and compliance measures, adhering to industry standards and regulations. Data analysis and provide insights to support decision making and strategy development. Abreast of emerging technologies and trends in data engineering, recommending, and implementing improvements to our data systems and processes. Implementing projects using Agile methodologies with adequate exposure to JIRA, Slack usage.