Data Modeller Job

Job Description

We are looking forward to hire Data Modeling Professionals in the following areas : Experience 8-10 Years Experience: 8-10 Years : -8-10 years of data modelling experience in a large enterprise. Dimensional data modeling is a key requirement. -Strong Communication Skills. -Should be good at translating business requirements into conceptual, logical, and physical data models. -Knowledge of Star-Schema Modelling, Snowflake Schema Modelling, Fact, and Dimension tables. -Demonstrable experience in modeling using a variety of techniques (3NF, dimensional, data vault, etc.) for different data stores and use cases using MS SQL SERVER / SNOWFLAKE. -Implement business and IT data requirements through new data strategies and designs across all data platforms (relational, dimensional, and NoSQL) and data tools (reporting, visualization, analytics). -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. -Design and implement reliable, scalable, robust, and extensible big data systems that support core products and business; -Own all data modeling efforts for an Analytic Theme within our Snowflake Analytic Platform, including the design of data structures and the identification of business transformation logic. -Ensure the consistency, availability, understanding, and performance of data by following and improving best practices and standards for data modeling. -Partner with data SMEs, data governance staff and architects and product owners to ensure that data meets consumption needs and conforms to governance standard methodologies. Required Technical/ Functional Competencies Domain/ Industry Knowledge: Working knowledge of customers business processes and relevant technology platform or product. Able to analyze current-state and define to-be processes in collaboration with SME and present recommendations with tangible benefits. Requirement Gathering and Analysis: Specialized knowledge of requirement management processes and requirement analysis processes, tools & methodologies. Extract requirements for complex scenarios and prototype independently. Identify modules impacted, features/functionalities impacted and arrive at high level estimates. Develop traceability matrix and identify transition requirements. Product/ Technology Knowledge: Implement code or configure/customize products, drive adoption of industry standards and practices, and contribute to development of reusable assets and innovative solutions. Conduct technical sessions and knowledge sharing sessions, and work on complex modules independently. Analyze various frameworks/tools and present recommendations, contribute to development of training and certification material, and demonstrate thought leadership through whitepapers and webinars. Architecture tools and frameworks: Specialized knowledge of architecture Industry tools & frameworks. Implement tools & framework in a complex scenario. Conduct tools & framework customization & tailoring workshop. Architecture concepts and principles: Specialized knowledge of architectural elements, SDLC, methodologies & customer business/domain. Establish Architectural principles/ patterns and use advanced tools to capture and analyze system/ technical issues. Analytics Solution Design: In Depth knowledge of statistical & machine learning techniques. Able to design analytical modelling approach for moderate-scale projects or for components of large-scale. Understand business requirements & constraints including potential trade-offs between speed & accuracy, maintains trends. Tools & Platform Knowledge: Familiarity with the wide range of data science/analytics commercial and open source software tools, their constraints, advantages, disadvantages, areas of application and mainstream packages relevant to technical stages of data science/analytics projects. Intermediate to advanced skills in programming languages used for data science/analytics and ability to apply these for data acquisition, pre-processing, modelling and model deployment. Ability to interpret and modify existing scripts and conduct quality checks. Required Behavioral Competencies Accountability: Takes responsibility for and ensures accuracy of results and deadlines of the function and/or team and in completing own work. Collaboration: Reaches out to others in team to ensure connections are made and team members are working together. Looks for ways to integrate work with other teams, identifying similarities and opportunities, making necessary changes in work to ensure successful integration. Agility: Demonstrates openness to the possibilities that change presents and begins to plan for how role may change. Works with others to prepare for change. Customer Focus: Identifies trends and patterns emerging from customer preferences and works towards customizing/ refining existing services to exceed customer needs and expectations. Communication: Communicates well-organized ideas, information, and data to broad and diverse audiences across the Organization (through formal and informal presentations). Helps others identify their appropriate audience. Drives Results: Sets realistic stretch goals for self & others and perseveres to follow through with resilience and remains calm in a crisis or stressful situation to exceed organization/client expectation. Resolves Conflict: Identifies and understands the source of conflict, addresses, and overcomes. Certifications Mandatory Our Hyperlearning workplace is grounded upon four principles Flexible work arrangements, Free spirit, and emotional positivity Agile self-determination, trust, transparency, and open collaboration All Support needed for the realization of business goals, Stable employment with a great atmosphere and ethical corporate culture