Data Modeler

2 months ago

Delhi, India Tredence Inc. Full time
8+ years of proven work experience in data modelling related projects as a Data Modeler.

Be responsible for the 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).

Experience in deploying data models in on-prem and cloud platforms – Microsoft Azure / Google Cloud Platform (GCP) / Amazon Web Services (AWS).

Experience in implementing various types of data modeling techniques – relational, dimensional modeling, building Entity-Relationship diagram with commendable understanding in relationship building, referential integrity, etc.

Well versed in data warehousing with proficiency in designing various kind of fact tables, dimension tables and putting them all together in star schema, snowflake schema, etc. based on business requirements. Experience in defining data loading strategy, SCD types, CDC methodologies.

Good hands-on experience on Enterprise Data Modeling and Retail Data Modeling with proven track record to implement relevant KPIs of Retail industry will be a plus.

Ability to understand data relationships and can design data models that reflects these relationships and facilitates efficient ingestion, processing, and consumption of Data.

Experience in implementation of multiple indexing strategies, partitioning techniques (preferable in Azure SQL Data Warehouse (Synapse)) as part of optimization.

Experience in certain industry leading data modeling tools will be an added advantage.

Excellent problem solving and communication skills; experience in interacting with technical and non-technical stakeholders at all levels.

Experience gathering business requirements and understanding and translating business needs into data models, creating logical and physical data models using best practices to ensure high data quality and reduced redundancy of Product Information.

Work proactively and independently to address project requirements and articulate issues/challenges to reduce project delivery risks.

Create logical and physical data models using best practices to ensure high data quality and reduced redundancy.

Understand and translate business needs into data models supporting long-term solutions. Working closely with the data analyst and data platform teams to Analyze and evaluate existing data systems and continuously updating and optimizing data models.

Working closely with Business and Analytics team to understand business needs and translate them into long-term solution data models.

Working with the data ingestion teams from different business areas to understand the data availability and format and to create conceptual data models and data flows.

Jointly accountable, together with Platform and Development teams, for the performance of the reporting solutions produced.

Developing best practices for data coding naming conventions, default values, semantics, data dictionary, etc. to ensure consistency within the system and act as an educator and ambassador for best practices when it comes to the data modelling.

Recommend opportunities for reuse of data models in new environments. Develop data models according to company standards.

Perform reverse engineering of physical data models from databases and SQL scripts. Evaluate data models and physical databases for variances and discrepancies.

Validate business data objects for accuracy and completeness. Analyze data-related system integration challenges and propose appropriate solutions.

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