Senior Data Science Engineer

About the job

As a Senior Data Science Engineer, you will play a critical role in designing, developing, and maintaining our data infrastructure and pipelines. Your expertise will drive the effective collection, storage, processing, and analysis of large-scale data sets, enabling actionable insights and data-driven decision-making across the organization. You will collaborate with cross-functional teams, including software engineers, and product managers, to deliver scalable and high-performance data solutions that support our growing product needs. Job Description in brief: • Design and Develop Data Solutions: Lead the end-to-end design and development of scalable, efficient, and reliable data pipelines and ETL workflows. Implement data integration processes from various sources to central data repositories. • Data Modeling and Architecture: Design and optimize data models for both structured and unstructured data, ensuring efficient data storage and retrieval. Evaluate and propose appropriate database technologies and data storage solutions. • Data Quality and Governance: Implement data quality checks and ensure data integrity across all data assets. Establish and maintain data governance practices to guarantee compliance with data security and privacy regulations. • Performance Optimization: Continuously monitor and enhance the performance of data pipelines, databases, and data processing systems to achieve optimal efficiency and low latency. • Big Data Technologies: Stay up-to-date with the latest trends and advancements in big data technologies, frameworks, and tools. Evaluate and introduce new technologies to improve data engineering processes. • Collaboration and Leadership: Collaborate with data scientists, analysts, and other stakeholders to understand their data needs and provide the necessary infrastructure and support. Mentor and guide junior data engineers to foster a high-performing data engineering team. • Documentation and Best Practices: Maintain comprehensive documentation for all data processes, methodologies, and configurations. Promote and enforce best practices in data engineering across the organization. • Troubleshooting and Support: Investigate and resolve data-related issues, including performance bottlenecks, data inconsistencies, and system failures.