Senior Data Engineer

Responsibilities:

   Implement scalable, secure, and efficient data architectures on on-prem and cloud platforms (Azure/GCP/AWS) to support business growth and data-driven decision-making. 

   Collaborate with data  engineers, and product teams to identify data requirements and develop data models that meet business needs. 

2. Data Ingestion and Integration: 

   Develop and maintain data ingestion pipelines using various tools and technologies, such as Apache Spark, PySpark, Kafka, and Flume. 

   Integrate data from multiple sources, including relational databases, NoSQL databases, APIs, and files. 

3. Batch and Stream Processing: 

   Develop and maintain batch and stream processing pipelines using tools like Apache Spark, Apache Flink, and Apache Beam. 

   Integrate with messaging systems, such as Apache Kafka, Amazon Kinesis, and Google Cloud Pub/Sub. 

4. SQL Knowledge: 

   Very strong SQL knowledge, including query optimization, indexing, and database design. 

5. Delta Lake and Data Warehouse: 

   Design and implement Delta Lake and data warehouse/mart solutions to support business intelligence, reporting, and analytics. 

   Develop and maintain data pipelines to ingest, process, and store data in Delta Lake and data warehouses. 

6. Distributed Databases and Data Warehousing: 

   Implement and maintain data warehouses, such as Amazon Redshift, Google BigQuery, and Azure Synapse Analytics. 

7. Database Design and Development: 

   Design, develop, and maintain efficient and scalable database systems across different platforms of relational databases (such as Oracle, MySQL, PostgreSQL, SQL Server). 

   Collaborate with cross-functional teams to understand data requirements and translate them into effective database solutions. 

   Implement and design data models and database schemas that align with business needs, ensuring data integrity and efficient data retrieval. 

   Develop and optimize database queries, stored procedures, and functions for maximum performance and responsiveness. 

8. Performance Tuning and Optimization: 

   Analyze and monitor database performance using diagnostic tools, identifying and resolving performance bottlenecks and inefficiencies. 

   Optimize data processing workflows and queries to improve performance, reduce latency, and increase throughput. 

9. Data Management: 

   Implement data archival mechanisms and data retention policies to ensure efficient data storage. 

   Ensure the security and integrity of data by implementing access controls, data encryption, and backup and recovery strategies. 

10. Automation and Integration: 

   Identify and implement automation solutions for data workflows. 

   Collaborate with the development team to integrate database solutions into software applications effectively. 

11. Data Mart and Data Lake: 

   Design and implement data marts and data lakes to support business intelligence, reporting, and analytics. 

   Develop and maintain data pipelines to ingest, process, and store data in data lakes, such as Apache Hadoop, Amazon S3, and Azure Data Lake Storage. 

12. CI/CD and Automation: 

   Develop and maintain automated testing, deployment, and monitoring scripts using tools like Jenkins, GitLab CI/CD, or similar. 

   Ensure continuous integration and delivery of data pipelines and applications. 

13. Data Analysis and Modeling: 

   Strong data analysis skills, including data modeling, data mining, and data visualization. 

   Collaborate with data modelers to develop and implement data models to drive business insights and decision-making. 

   Analyze complex data sets to identify trends, patterns, and correlations. 

14. Exploration of New Tools: 

   Ability to explore new tools and technologies, and quickly develop proof-of-concepts (POCs) for data engineering open-source tools. 

15. Documentation: 

   Document database design, configurations, and technical specifications. 

Requirements: 

• Bachelor's degree in Computer Science, Information Technology, or a related field. Relevant certifications are a plus. 
• 8-10 years of experience in data engineering, with a focus on cloud-based data architectures (Azure/GCP/AWS). 
• Strong proficiency in SQL  and experience with various database management systems, including both relational and NoSQL databases. 
• In-depth knowledge of database performance optimization techniques, including query optimization, indexing, partitioning, and caching. 
• Familiarity with database archival mechanisms and data retention strategies. 
• Solid understanding of database security principles and best practices. 
• Experience with database administration and monitoring tools. 
• Strong analytical and problem-solving skills, with the ability to diagnose and resolve complex database issues. 
• Excellent communication and collaboration skills, with the ability to work effectively in a team environment. 
• Detail-oriented mindset with a commitment to delivering high-quality work. 
• Ability to adapt to changing priorities and manage multiple projects simultaneously.