Hadoop, Spark, Scala

Job description

**Desired Competencies (Technical/Behavioral Competency)** **Must-Have:** - Strong experience in programming languages such as Python, Java, or Scala for data manipulation and engineering tasks. - Expertise in SQL and NoSQL databases - Hands-on experience with big data technologies like Hadoop, Spark, Kafka, and Hive to handle large-scale data processing and real-time data streams. - In-depth knowledge of data warehousing solutions such as Amazon Redshift, Google BigQuery, and Snowflake for building and managing data warehouses. - Proficiency in designing, developing, and maintaining ETL (Extract, Transform, Load) processes using tools like Apache NiFi, Talend, or Informatica. **Good-to-Have:** - Familiarity with cloud platforms like AWS, Azure, or Google Cloud for deploying, managing, and scaling data infrastructure and services. - Strong understanding of data modeling concepts and techniques to create efficient and scalable data models. - Experience with version control systems such as Git for code management and collaboration. - Knowledge of data governance, data quality standards, and data security practices to ensure compliance and protection of sensitive information **Responsibility of / Expectations from the Role** **1** Design, develop, and maintain scalable data pipelines for extracting, transforming, and loading data from various sources to ensure seamless data flow and accessibility. **2** Collaborate with cross-functional teams to integrate data from multiple disparate sources, ensuring consistency, accuracy, and reliability of data. **3** Optimize data processing workflows and storage solutions for performance, scalability, and cost-efficiency **4** Implement data quality checks and validation processes to ensure the accuracy, completeness, and consistency of data throughout the data lifecycle. **5** Monitor the performance of data pipelines and infrastructure, identifying and resolving issues to maintain system stability and reliability.