Job Description :
Role : Data Modeler
Location: Dallas, TX
Duration: 1+ year
H1s are eligible

Local candidates who can do an onsite interview only

Qualifications:
Required Qualifications

1. Bachelor’s degree in Computer Information Systems, Management Information systems, or Computer Science or related job experience.
2. 10+ years of enterprise data architecture and modeling experience. Must have experience building and managing enterprise grade data models. Experience with relational modeling, domain modeling, and dimensional modeling. Experience with metadata management and its use for modeling activities.
3. 10+ years in conducting Data Analysis, Data Profiling and Data Discovery on dispersed operational source systems.
4. 10+ experience with creating data model and data structure in Oracle, SQL Server, PostgreSQL using ER/Studio DA or Erwin.
5. 8+ years of experience using the following modeling techniques: 3rd Normal form, ERD, Dimensional modeling, Time series, MDM data block.
6. 8+ years of experience with data management standards and data model practices
7. Thorough knowledge of conceptual and logical data modeling and familiarity with physical database design and optimization concepts.
8. Experience with common standards and best practices for data elements, data types, and file formats.
9. Knowledge of established data management and data governance principles.
10. Ability to conceive and describe the big picture.
11. Strong quantitative and analytic skills. Proven ability to quickly work with multiple key stakeholders.
12. Outstanding problem-solving and critical-thinking skills.
13. Able to steer through a large corporate environment and ensure appropriate organizational data processes are met.
14. Data analysis/profiling and reverse engineering of data.
15. Strong knowledge of relational and multi-dimensional database architectures.
Preferred Qualifications
1. Commercial real estate industry experience.
2. Data integration experience leveraging tools such as SSIS, Informatica, IBM DataStage and open source ETL/ELT tools.
3. Relational database administration experience.
4. Familiarity with open source data technologies.
5. Familiarity with emerging technologies like NoSQL, predictive analytics, data visualization, and variety of data formats.

Responsibilities:
ESSENTIAL RESPONSIBILITIES
1. Facilitate and/or participate in agile sessions to assess, capture, and translate complex business issues and requirements into structured data architecture and data modeling use case requirements.
2. Hold conceptual and logical data model reviews with product managers and data subject matter experts.
3. Must be able to abstract general principles from specifics and conceptualize and design data model.
4. Develop, enhance, and manage conceptual data model, enterprise logical data model (LDM), physical data model (PDM) for the data platform. Ensure data lineage mapping across implementations for the platform. Understand and meet referential data integrity requirements based on business needs. Develop and/or assist in generating source to target data mapping.
5. Perform data model consolidation and integration to integrate new silos of organizations source data to the data platform.
6. Understand and apply logical entity-relational design concepts. Develop dimensional models and views needed for data analytics and reporting.
7. Develop and maintain fully defined conceptual, logical and physical dimensional data models to ensure the information models are capable of meeting end user and developer needs.
8. Develop data models and data migration strategies utilizing best practice concepts of data modeling including star schema, snowflake schema, etc.
9. Build model aggregation layers and specific star schemas as subject areas within a logical and physical model.
10. Enhance enterprise data platform data model which is the enterprise level data model to support organizations evolving business needs. The enterprise data platform serves as the foundation for interface and system integration and reporting across the organization.
11. Document, develop, and maintain data models in Erwin or equivalent modeling tools.
12. Participate in business process modeling, working with the developers, with respect to data mapping and validation of the data’s life cycle. Effectively negotiate and influence project direction with alignment to data modeling and data management standards.
13. Perform data profiling to detect patterns, trends and anomalies, to gain a more in-depth understanding of data requirements and need to ensure model is fit-for-use.
14. Map existing or legacy logical entities to the new data platform environment, to facilitate enterprise transition.
15. Provide clear, concise, accurate, and timely communication to management, peers, and customers.
16. Adopt, support, and participate in the implementation of the Enterprise Data Management Strategy.
17. Deal with vendor-related issues concerning the support, implementation, upgrade and impact analysis, evaluation, and selection of third-party products.
18. Manage and establish common business vocabulary, standards for naming and abbreviation conventions, data definitions, ownership, documentation, procedures, and techniques adhere to defined company standards and best practices.
19. Provide Level of Efforts (LOEs), activity status, identify dependencies impacting deliverables, identify risks and mitigation plans, and identify issues and impacts during agile sessions for data architecture and data modeling stories.
20. Work both independently and in collaboration with multiple scrum teams to enhance and assist in building of the enterprise data platform.
21. Develop and deliver presentations, summarizing key business needs and articulating model options to facilitate model optimization decisions and to drive decision-making with core team of key stakeholders.
22. Work with DBA’s in implementing the physical data model artifacts. Assist DBA’s in optimizing the enterprise data platform environment.
23. Organize data at a macro level and establish and determine subject areas at what level they are managed in relation to master data.
24. Promote data models into several data platform environments.
25. Proficient in performing data quality assessments and use the information to build enterprise grade data models.
26. Should ensure that the data work products being produced adhere to data quality standards
27. Experience working in an agile environment and scrum practices.

Local candidates who can do an onsite interview only

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