Data Engineer (Azure) Jobs

Requirements

• Expertise in data modeling principles/methods including conceptual, logical & physical Data Models.
• Strong ETL/ELT experience.
• In depth knowledge of different modeling methodologies and techniques for OLAP and OLTP systems (ER Modeling, Dimensional Modeling).
• Knowledge of Data modeling tools like Erwin (or similar) to design Conceptual, Logical and Physical Data models.
• Advanced SQL knowledge on concepts like Indexing, Partitioning, Constraints, Keys etc to design optimal data warehouse
• Must be proficient in normalization and denormalization concepts to model different layers of DWH.
• Solid knowledge of using languages such as SQL, Python
• Apps and Synapse Dedicated SQL pool.
• Hands-on software development skills e.g. DevOps
• Solid knowledge of different data storage types e.g. SQL Server, Data lake, No-SQL databases.
• Experience with Azure Synapse, Azure Data lake, Azure Data Factory, Azure Logic
• In-depth knowledge and understanding of all layers of Datawarehouse (Landing,Stage, Dimensional, Semantic, Datamart)

Responsibilities

• Design, build and maintain data stores, warehouses, lakes or marts
• Transform the logical representation of the model into a physical representation by defining physical names,data types for each attribute and entities.
• Build analytical solutions using efficient data collection procedures
• Optimize existing data pipelines
• Determine how the various entities are related and develop conceptual diagrams that represent the connections among the entities.
• Design the right set of distribution strategy ,indexes,partitions and keys (surrogate,Natural keys etc) in order to have optimal performance level for data storage and retrieval.
• Work well as a team to properly document solutions and designs
• Collaborate with the BI and Analytics teams on creating the optimized data mart and semantic layer.
• Recommend and establish standards, guidelines and best practices around modeling of dimensional, semantic and data mart layer(e.g. naming convention) to ensure consistency within the system.
• Work closely with the data engineers to implement data warehouse and ETL Pipelines
• Develop a logical data model and validate the model to ensure that it serves the needs of the business application and its consumers.
• Consolidate multiple data sources into an integrated platform