vacant for Snowflake Developer - ETL/Data Warehousing

Job Description

Role And Responsibilities Of A Snowflake Data EngineerA Snowflake Data Engineer is expected to perform the following duties and responsibilities:1. Build a Data ArchitectureData architectures need to be planned, created, and maintained in a systematic manner, and aligned with business needs.2. Collect DataData must be obtained from suitable sources before any work on the Database can begin. Data Engineers develop a set of algorithms for storing optimized data after formulating a set of dataset processes.3. Conduct ResearchTo deal with any business challenges that might arise, Data Engineers conduct research in the industry.4. Improve SkillsA Snowflake Data Engineer doesnt just rely on theoretical concepts. It is essential that they possess the abilities to work across any development environment, regardless of the language used. As well, they need to stay current with machine learning and its algorithms, such as k-means, random forests, and decision trees.Furthermore, they should be proficient with analytics tools such as Tableau, Knime, and Apache Spark. Using these tools they generate useful insights from data for businesses of all sizes. 5. Identify Patterns And Create ModelsHistorically relevant insight is extracted from data using a descriptive data model. They also develop predictive models using forecasting techniques in order to provide actionable insights for the future. As well, users can use recommendations that are tailored to different outcomes, utilizing a prescriptive model. Data Engineers spend a great deal of time identifying trends in their stored data. 6. Automate TasksEngineers analyze data and identify manual tasks that can be automated to reduce manual effort.Roles And Responsibilities Of A Snowflake Data Engineer :- Snowflake Inc.s Data Engineers will be responsible for creating very large-scale data analytics solutions based on the Snowflake Data Warehouse. The ability to design, implement, and optimize large-scale data and analytics solutions on Snowflake Cloud Data Warehouse is essential. Expertise with Amazon Redshift is a must. A Data Engineer at Snowflake is responsible for :- Implementing ETL pipelines within and outside of a data warehouse using Python and Snowflakes Snow SQL- Querying Snowflake using SQL.- Development of scripts using Unix, Python, etc. for loading, extracting, and transforming data.- Knowledge of Amazon Web Services Redshift- Assist with production issues in Data Warehouses like reloading data, transformations, and translations- Develop a Database Design and Reporting Design based on Business Intelligence and Reporting requirementsData Engineer Skills :- Every specialist has specific skills based on the responsibilities they have. The skillset of a Snowflake Data Engineer can vary, nevertheless, they engage in three basic activities: engineering, data science, and Database/warehouse management.Engineering Skills- Data analysis and big data systems such as Hadoop and Apache Hive are usually written in Java or Scala. For training and implementing machine learning models, Data Engineers use high-performance languages like Python, R, and Golang. The popularity and clarity of Python and R lang make them popular choices for data projects. Software architecture :- Java- Scala- Python- R- C/C#- Golang- Data-Related Expertise Together, Data Engineers and data scientists would work closely. In order to work effectively with data platforms, you need a deep understanding of data models, algorithms, and data transformation techniques. Engineers will be responsible for building ETL tools (extracting, transforming, and loading), storage, and analytics tools. It is therefore imperative that you have experience with current ETL and BI solutions.Using Kafka or Hadoop for big data projects requires more specific expertise. Data Engineers need knowledge of machine learning libraries and artificial intelligence frameworks. These include TensorFlow, Spark, PyTorch, and MLPack.- A solid understanding of data science concepts is required- Data analysis expertise- Working knowledge of ETL tools- Knowledge of BI tools- Experience with Big Data technologies such as Hadoop and Kafka- Extensive experience with ML frameworks and libraries including TensorFlow, Spark, PyTorch, and MLPACK,

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web servicesamazon web servicesdata modelsdata analysisbig data