Snowflake Data Engineer

Role: Snowflake Data Engineer

Location: Remote

Client: Kimberly Clark

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-oriented/object 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 [ETL/ELT, data replication/CDC, 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