- Create and maintain large-scale data processing systems and infrastructure.
- Build robust, performant, and scalable data pipelines to ingest, transform, and store complex data from multiple sources.
- Collaborate with data science and software engineering team members to provide required data in an accessible, timely, and accurate manner.
- Optimize and refine processes, algorithms, and systems to enhance data quality and reliability.
- Ensure data privacy and security compliance.
- Implement monitoring, logging, and alert systems to ensure data pipeline health and performance.
- Collaborate with infrastructure and IT teams to ensure optimal data storage and retrieval mechanisms.
- Drive the optimization, testing, and tooling to improve data quality.
- Mentor junior data engineers, imparting knowledge and promoting best practices.
- Stay updated with emerging technologies and introduce them as needed to improve the data engineering ecosystem.
Requirements :
- Bachelor's degree in Computer Science, Engineering, or a related field. Master's degree is a plus.
- 5+ years of experience in data engineering, ETL processes, and database systems.
- Proficient in SQL, Python, PySpark, and experience with big data platforms.
- Experience creating data pipelines and working with workflow management tools.
- Strong experience with relational and NoSQL databases.
- Working experience in cloud platforms like AWS and Azure.
- Excellent communication and collaboration skills.
- Experience with Databricks platform, Azure Data Factory, Kedro framework.
- Experience with Machine Learning processes.
- Relevant Industry Certification.
Role Description
The Senior Data Engineer will play a pivotal role in building, optimizing, and maintaining our data pipeline architecture, ensuring data quality and accessibility for cross-functional teams. This role will be a key member of our Advanced Analytics team and will focus on building and supporting our data pipelines in a Databricks environment.
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