DATA ENGINEER

Job Description:

Job Description: Experience: 3-5 years Work Mode: Remote Shift time: 5 am to 2 pm Joining: Immediate

Job Summary: This role is for someone who builds and manages data warehouses (DWH) using Microsoft Azure services. The person should be strong in designing how data is structured (schemas), how data moves from source to warehouse (ETL/ELT), and how it's used in reporting tools like Power BI.

Key Responsibilities

● Data Warehouse (DWH) Development Expertise: Proven experience in designing, implementing, and optimizing data warehouse solutions, including understanding of ETL/ELT processes within Azure.

● Dimensional Modeling (Kimball & Inmon): In-depth knowledge of designing star schemas, snowflake schemas, facts (transactional, snapshot, accumulating), and various dimension types (SCD Type 1, 2, 3, 6) for optimal query performance and data integrity. ● Microsoft Azure Data Services: Hands-on experience with core Azure data platform components relevant to DWH administration, such as Azure Synapse Analytics (dedicated SQL pools), Azure Data Factory, Azure Data Lake Storage, and Azure SQL Database.

● Business Intelligence (BI) Tool Integration & Support: Familiarity with connecting DWH to and troubleshooting issues with popular BI tools (e.g., Power BI, Tableau, Qlik Sense) and understanding how the DWH design impacts BI reporting.

● DevOps Principles & Practices: Understanding of continuous integration/continuous deployment (CI/CD) pipelines for data solutions, automated testing, and infrastructure as code (IaC) concepts.

● Git Version Control: Proficient in using Git code management, branching strategies, pull requests, and collaborative development within a team environment.

● Data Pipelining and ETL/ELT Orchestration: Experience in building, monitoring, and maintaining robust data pipelines using tools like Azure Data Factory for efficient data ingestion, transformation, and loading into the DWH.

● Performance Tuning & Optimization: Ability to identify and resolve DWH performance bottlenecks, optimize queries, manage indexes, and configure resources within Azure Synapse Analytics for efficient data processing and querying.

● Data Governance & Security: Awareness of data quality, data lineage, and implementing security best practices (e.g., row-level security, column-level security) within the Azure DWH environment

● Excellent communication is a must