Data Modeler

Job Description:-

NOTE:-

We need a data modeler for NoSQL Databases (not necessarily Mongo only can be other NoSQL Databases also)
This is not a DBA position for Mongo this is primarily a data modeler need to work with unstructured data (JSON file formats).
There is no relational data here so the thought process of the ideal candidate should be aligned to be able to work with Big Data, NoSQL data which is not relational.

Your Impact

Meet with stakeholders to understand and document requirements which are the basis for the model solutions

Provide training and mentorship, and work closely with product analysts on the product teams, on data mapping

Develop model solutions, and be able to convey the pros and cons of alternatives

Review model solutions crafted by the team members to ensure alignment to standards and consistency with Industry standards and best practices

Ensure consistency between the MongoDB collections

Ensure maintenance of controls, data exchange management

Ensure/lead model reviews to ensure fit for use (satisfies requirements, both immediate and likely future)

Ensure modeling team meets all commitments and uphold the motto of no delays to product teams

Ensure continued creation/maintenance of critical modeling artifacts, ex. requirements and modeling analysis documentation, model change requests logs, data dictionaries, artifacts for architecture document, etc.

Achieve data standardization for critical value sets through acquiring agreement and buy-in from stakeholders and enabling compliance through a streamlined process

Identify and document patterns, especially in domain and enterprise models, ensuring alignment and/or ease for integration (common language), and escalate blockers to Sr. Manager.

Support data governance work as needed.

Responsibilities

Designs, implements, and documents data architecture and data modeling solutions, which include the use of MongoDB and other NoSQL databases.

Development of the conceptual, logical, and physical data models, the implementation of RDBMS, operational data store (ODS), data marts, and data lakes on target platforms (SQL/NoSQL).

Oversee and govern the expansion of existing data architecture and the optimization of data query performance via best practices.

Implement business and IT data requirements through new data strategies and designs across all data platforms (relational, dimensional, and NoSQL) and data tools.

Work with business and application/solution teams to implement data strategies, build data flows, and develop conceptual/logical/physical data models.

Define and govern data modeling and design standards, tools, best practices, and related development for enterprise data models.

Identify the architecture, infrastructure, and interfaces to data sources, tools supporting automated data loads, security concerns, analytic models, and data visualization.

Skills Needed

8-10 years data modeling experience with 5+ years of hands-on relational, dimensional, and/or analytic experience (using MongoDB and other NoSQL data platform technologies, and ETL and data ingestion protocols).

Specialized technical knowledge of the MongoDB platform or similar NoSQL technologies

Experience with data warehouse, data lake, and enterprise big data platforms in multi-data-center contexts required.

Good knowledge of metadata management, data modeling, and related tools (Erwin or ER Studio or others) required.

Design and development experience building a reusable REST API model/framework to consume data from and/or push data into MongoDB (or similar technology)

In depth knowledge of modeling/architectural patterns, governance methodologies, and potential limitations within MongoDB

Ability to configure schema and MongoDB data modelling

Experience in Database security management

Knowledge of MongoDB administration and installation in AWS and Red Hat

In-depth understanding of MongoDB architecture

Excellent communication skills; ability to communicate with various stakeholders at various levels
Desired Skills and Experience
MongoDB, NoSQL, ETL, ingestion, Database, methodologies