Data Modeler

About Us:

At Parkar, we stand at the intersection of innovation and technology, revolutionizing software development with our cutting-edge Low Code Application Platform, For almost a decade, our expertise has expanded to four countries, offering a full range of software development services, including product management, full-stack engineering, DevOps, test automation, and data analytics.

, our pioneering Low Code Application Platform, redefines software development by integrating over 500 modular code components. It covers UI/UX, front-end and back-end engineering, and analytics for a streamlined, efficient path to digital transformation through standardized software development and AIOps.

Our commitment to innovation has earned the trust of over 100 clients, from large enterprises to small and medium-sized businesses. We proudly serve key sectors like Fintech, Healthcare-Life Sciences, Retail-eCommerce, and Manufacturing, delivering tailored solutions for success and growth.

At Parkar, we don't just develop software; we build partnerships and pave the way for a future where technology empowers businesses to achieve their full potential.

For more info., Visit our website:

Role Overview:

The data modeler plays a crucial role in designing, developing, and maintaining data models to support efficient data management and analytics within the organization. This role is essential in optimizing data architecture, translating business initiatives into robust data models, and enhancing data integrity, availability, and performance. The data modeler is responsible for developing conceptual, logical, and physical data models.

As a data analyst this role interprets and analyses complex datasets to provide insights and support data-driven decision making within an organization. This role works alongside engineers, modelers, and business executives to gather and transform data, conduct statistical analyses, and create visualizations and reports that will contribute to identifying trends, patterns, and opportunities that drive business initiatives.

Responsibilities:

• Understand business requirements and translate them into data requirements.
• Analyze existing data structures and determine gaps, if any, to meet new requirements across all ongoing initiatives.
• Actively participate in all ongoing initiatives related to the data and analytics ecosystem, including capital projects, operational projects, and proof of concept work.
• Profile source data by retrieving and gathering raw data from various internal and external source systems, to identify trends, patterns, and quality issues.
• Design, optimize, and maintain conceptual, logical, and physical data models to support efficient data management and analytics within the organization.
• Collaborate with data architects, data engineers, report developers, AI/ML engineers, and business stakeholders/executives.
• Map out data personas and consumer profiles to ensure a better understanding of customer needs.
• Design, create, and implement logical and physical data models for both IT and business solutions to capture the structure, relationships, and constraints of relevant datasets.
• Build and operationalize complex data solutions, correct problems, apply transformations, and recommend data cleansing/quality solutions.
• Effectively collaborate and communicate with various stakeholders to understand data and business requirements and translate them into data models.
• Create entity-relationship diagrams (ERDs), data flow diagrams, and other visualization tools to represent data models.
• Collaborate with database administrators and software engineers to implement and maintain data models in databases, data warehouses, and data lakes.
• Develop data modeling best practices and use these standards to identify and resolve data modeling issues and conflicts.
• Conduct performance tuning and optimization of data models for efficient data access and retrieval.
• Incorporate core data management competencies, including data governance, data security and data quality.

Requirements:

• Bachelor's degree in related field.
• Hands-on experience in data gathering, profiling, data modeling and data analysis.
• Expert knowledge of data modeling concepts, methodologies, and best practices & proficiency in data modeling tools. Familiarity with dimensional modeling and data warehousing concepts
• Expert level of proficiency in designing SQL queries and Python scripts.
• Hands-on relational, dimensional, and analytical project experience using RDBMS (SAP, Oracle, SQL Server), NoSQL (Cassandra, Mongo) data platform technologies, and ETL.
• Robust experience with databases like SAP, Oracle, SQL Server, etc. and Big Data/Cloud platforms like Microsoft Azure, Google Big Query, Databricks, AWS, Cloudera, etc.
• Experience with unstructured, semi-structured, and structured data.
• Experience with Business Intelligence/Data visualization and Reporting tools such as Power BI.
• Expert knowledge of statistical analysis techniques and concepts for both descriptive and inferential statistics.
  
  Knowledge of data visualization is the best practice to effectively communicate insights.

Education:

• Bachelor's degree in computer science, engineering or similar domain.