8+ years of IT experience with 4+ years in data management (data integration, data modeling, data quality, and optimization)
Strong experience with advanced analytics tools and scripting languages (R, Python, Java, C++, Scala)
Proven ability to design, build, and manage data pipelines, data models, schemas, metadata, and workload management
Experience with SQL and database technologies for relational and non-relational data systems and relevant certifications (e.g., Snowflake)
Requirements:
Build data pipelines and optimize data structures, schemas and metadata
Drive automation through effective metadata management and modern data preparation, integration and AI-enabled techniques
Collaborate across departments and train counterparts in data pipelining and preparation to enable data consumption
Participate in ensuring compliance and governance during data use
Job description
Visa status: U.S. Citizens and those authorized to work in the U.S. are encouraged to apply.
Tax Terms: W2, 1099
Corp-Corp or 3rd Parties: Yes
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
