Snowflake Data Engineer

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timePosted 3 days ago location United States salarySalary undisclosed
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Job Description

Job Description At least 8 years of IT experience and 4 years or more of work experience in data management disciplines including data integration, modeling, optimization and data quality. Strong experience with advanced analytics tools for Object-orientedobject function scripting using languages such as R, Python, Java, C++, Scala, others. Strong ability to design, build and manage data pipelines for data structures encompassing data transformation, data models, schemas, metadata and workload management. Strong experience with popular database programming languages including SQL, Blob Storage and SAP HANA for relational databases and certifications on upcoming MS Snowflake HDInsights, Cosmos for non-relational databases. Strong experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures and integrated datasets using traditional data integration technologies. These should include ETLELT, data replicationCDC, message-oriented data movement, API design and access and upcoming data ingestion and integration technologies such as stream data integration, CEP and data virtualization. Strong experience in working with and optimizing existing ETL processes and data integration and data preparation flows and helping to move them in production. Strong experience in streaming and message queuing technologies such Snowflake Service Bus, and Kafka. Basic experience working with popular data discovery, analytics and BI software tools like Tableau, Power BI and others for semantic-layer-based data discovery. Strong experience in working with data science teams in refining and optimizing data science and machine learning models and algorithms. Demonstrated success in working with large, heterogeneous datasets to extract business value using popular data preparation tools. Demonstrated ability to work across multiple deployment environments including cloud, on-premises and hybrid, multiple operating systems and through containerization techniques such as Docker, Kubernetes. Interpersonal Skills and Characteristics Strong leadership, partnership and communication skills Ability to coordinate with all levels of the firm to design and deliver technical solutions to business problems Ability to influence without authority Prioritization and time management Data modelling with Enterprise Data Warehouse and DataMart, Snowflake Data Lake Gen2 BLOB., Data engineering experience with Snowflake Databricks Hands-on experience in SQL, Python, NoSQL, JSON, XML, SSL, RESTful APIs, and other formats of data viz parquet, ORC, AVRO Hands-on emphasis with a proven track record of building and evaluating data pipelines, and delivering systems for final production Exposure to Big Data Analytics (data and technologies), in-memory data processing using spark. Working Experience with various data bases like SAP HANA, Cassandra, Mangodb Strong understanding DevOps, on-premise, and cloud deployments Roles and responsibilities Build Data Pipelines Drive Automation through effective metadata management Learning and applying modern data preparation, integration and AI-enabled metadata management tools and techniques. Tracking data consumption patterns. Performing intelligent sampling and caching. Monitoring schema changes. Recommending - or sometimes even automating - existing and future integration flows. Collaborate across departments train counterparts in these data pipelining and preparation techniques, which make it easier for them to integrate and consume the data they need for their own use cases. Participate in ensuring compliance and governance during data use