Responsibilities

• Designing and developing data warehouse solutions: Collaborate with stakeholders, including business analysts and data scientists, to understand data requirements and translate them into efficient data warehouse designs. Develop data models, schema definitions, and ELT (Extract, Load, Transfer) processes to populate and maintain the data warehouse.
• ELT Development: Design, develop, and optimize ELT processes to extract data from various sources, transform it to match the data warehouse schema, and load it into the data warehouse. Ensure the ETL processes are efficient, scalable, and reliable.
• Data Integration: Integrate data from disparate sources such as databases, flat files, APIs, and streaming data into the data warehouse. Implement data validation and cleansing processes to ensure data accuracy and consistency.
• Performance Optimisation: Monitor and tune the performance of data warehouse systems to ensure efficient query processing and data retrieval. Identify and resolve performance bottlenecks by optimizing database schema, indexes, and query execution plans.
• Data Governance and Security: Implement and enforce data governance policies, ensuring data quality, integrity, and security within the data warehouse. Define access controls and security measures to protect sensitive data.
• Data Analysis and Reporting: Work closely with data analysts and business intelligence teams to understand reporting requirements and provide them with the necessary data extracts, views, and data marts. Support ad-hoc data analysis and reporting needs.
• Documentation and Documentation: Document data warehouse designs, data models, ETL processes, and system configurations. Maintain up-to-date technical documentation for reference and knowledge sharing.
• Troubleshooting and Maintenance: Monitor data warehouse systems for issues, troubleshoot and resolve them promptly. Perform regular maintenance activities such as backups, patching, and system upgrades.
• Stay Current with Industry Trends: Keep up-to-date with the latest advancements and trends in data warehousing technologies, tools, and best practices. Evaluate and recommend new tools and technologies that can enhance the data warehousing capabilities of the organization.
  
  

Requirements

• Bachelor's degree in Computer Science, Information Systems, or a related field (or equivalent work experience).
• Proven experience of a minimum of 3+ years as a data engineer or data warehousing engineer.
• Strong knowledge of data warehousing concepts, methodologies, and best practices.
• Proficiency in SQL and experience with relational databases (e. g., Oracle, SQL Server, PostgreSQL).
• Hands-on experience with ETL tools (e. g., Informatica, Talend, SSIS, Power BI) and data integration techniques.
• Familiarity with data modeling and database design principles.
• Knowledge of data governance, data security, and privacy practices.
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities.
• Ability to work independently and in a team environment.
• Familiarity with cloud-based data warehousing solutions (e. g., Amazon Redshift, Google BigQuery) is a plus.
• Knowledge of programming languages like Python is beneficial.
• Primary Stack: AWS, ETL/ELT, Athena/Glue/EMR, spark/pyspark, Dynamodb, Streaming & CDC
  
  

This job was posted by Apurva Shrivastava from IndiGG.