Data Engineer

Data Engineering, Data Governance, Data Warehousing, Data Integration, Python, PySpark, AWS, Snowflake, AWS Glue, Lambda, DynamoDB, S3, Kinesis, RDBMS, NoSQL, Power BI 1. Design and develop data pipelines that extract data from various sources, transform it into the desired format, and load it into data storage systems. 2. Integrate data from multiple sources such as databases, data warehouses, APIs, and external systems. 3. Ensure data consistency and integrity, performing data validation and cleaning as needed. 4. Transform raw data into usable formats through data cleansing, aggregation, and enrichment techniques. 5. Optimize data pipelines for performance, scalability, and efficiency. 6. Monitor and tune data systems, resolving performance bottlenecks and implementing strategies like caching and indexing. 7. Implement data quality checks within data pipelines for accuracy and completeness. 8. Establish data governance for algorithms used in analysis and automated decision-making. 9. Prepare/maintain documentation around best practices covering data architecture, security, performance, and scalability. 10. Collaborate with business and technical teams, demonstrating strong analytical and communication skills. 11. Additional skills: Experience with Power BI Analytical tool is a plus.