Senior Data Engineer

Responsibilities:

• Leading design and development of data and analytics cloud-based platform. Crafting integrated systems, implementing ELT/ ETL jobs to fulfil business deliverables. Performing sophisticated data operations such as data orchestration, transformation, and visualization with large datasets. You will be working with product managers to ensure superior product delivery to drive business value & transformation. Demonstrating standard coding practices to ensure delivery excellence and reusability.
• Data Ingestion: Develop and maintain data pipelines to extract data from various sources and load it into Azure and Databricks environments.
• Data Transformation: Design and implement data transformation processes, including data cleansing, normalization, and aggregation, to ensure data quality and consistency.
• Data Modeling: Develop and maintain data models and schemas to support efficient data storage and retrieval in Azure and Databricks platforms.
• Data Warehousing: Design and build data warehouses or data lakes using Azure services such as Azure Data Lake Storage, Databricks DeltaLake
• Data Integration: Integrate data from multiple sources, both on-premises and cloud-based, using Azure Data Factory or other relevant tools.
• Data Governance: Implement data governance practices, including data security, privacy, and compliance, to ensure data integrity and regulatory compliance.
• Performance Optimization: Optimize data pipelines and queries for improved performance and scalability in Azure and Databricks environments.
• Monitoring and Troubleshooting: Monitor data pipelines, identify and resolve performance issues, and troubleshoot data-related problems in collaboration with other teams.
• Data Visualization: Build BI reports to enable faster decision making.
• Collaboration: Work with product managers to ensure superior product delivery to drive business value & transformation
• Documentation: Document data engineering processes, data flows, and system configurations for future reference and knowledge sharing.
• Job Qualifications
• Experience: Bachelor's or master's degree in computer science, data engineering, or a related field, along with 4+ year work experience in data engineering and cloud platforms.
• Azure and Databricks: Strong proficiency in Azure services such as Azure Data Factory, Azure Databricks, Azure SQL Database, Azure Data Lake Storage, and Azure Synapse Analytics.
• ETL Tools: Experience with ETL (Extract, Transform, Load) tools and frameworks, such as Apache Spark, Databricks Delta, or Azure Data Factory, for data integration and transformation.
• Programming: Proficiency in programming languages such as PySpark, Python, SQL, or Scala for data manipulation, scripting, and automation.
• Data Modeling: Knowledge of data modeling techniques and experience with data modeling tools.
• Database Technologies: Familiarity with relational databases (e.g., SQL Server) for data storage and retrieval.
• Data Warehousing: Understanding of data warehousing concepts, dimensional modeling, and experience with data warehousing technologies such as Azure Synapse Analytics or Azure SQL Data Warehouse or Azure Databricks DeltaLake
• Data Governance: Knowledge of data governance principles, data security, privacy regulations, and experience implementing data governance practices.
• Data Visualization: Experience of working with Microsoft Power BI to build semantic data model & BI reports/dashboards.
• Cloud Computing: Familiarity with cloud computing concepts and experience working with cloud platforms, particularly Microsoft Azure.
• Problem-Solving: Strong analytical and problem-solving skills to identify and resolve data-related issues.
• Proficiency in DevOps Tools and CICD tools (e.g. Azure DevOps, Chef, Puppet, Github)
• Job Schedule
• Job Number
• Job Segmentation