Data Modeling (8979-1) Las Vegas, NV $$$ bonus

: Bigdata, Hadoop, Snowflake, Data Vault 2.0, Data Lakehouse, Hive, S3, Erwin, Redshift, Oracle, SQL Server, Kimball, Inmon, Data Governance

If you post this job on a job board, please do not use company name or salary. Experience level: Mid-senior Experience required: 8 Years Education level: Bachelors degree Job function: Information Technology Industry: Information Technology and Services Pay rate : View hourly payrate Total position: 1 Relocation assistance: No Visa sponsorship eligibility: No

Job Description

• 7 + years of experience in Data Modeling & Data Analysis and should have excellent communication and leadership skills.
• Financial Domain knowledge is plus.
• Should have strong understanding of cloud Datawarehouse like Snowflake and Data modelling skill using Data Vault 2.0.
• Should have in depth understanding or executed new concepts like Data Lakehouse and should have worked on Big Data sources / tools such as Hive , S3 , Trino , HUE, etc.
• Should have done complex (100+ entities/tables) Data Modeling programs in both on-prem and cloud environments.
• Experience in designing normalized, denormalized, relational, dimensional, star and snowflake schemas for cloud, big data and on prem databases using any one of these data modeling tools (Erwin, ER/Studio, Toad Data Modeler, etc.)
• Extensive Data Modeling experience in same or similar bigdata and cloud databases such as Hive, Redshift, Snowflake and on prem databases such as Oracle, SQL Server and DB2
• Should have experience in working with product managers, project managers, business users, applications development team members, DBA teams and Data Governance team daily to analyze requirements, design, development, and deployment technical solutions.
• Should have implemented Bill Inmon and Ralph Kimball methodologies to design data warehouses
• Should have in depth understanding on Data Warehousing, Data Analysis, Data Profiling, Master Data Management (MDM), Data Quality, Data Lineage, Data Dictionary, Data Mapping, Data Policy, and Data Governance.