| Job Description | We are looking for a Senior Data Engineer with deep expertise in ETL development, data warehousing, and strong skills in Python and SQL. The ideal candidate will have over 8 years of hands-on experience in developing robust ETL pipelines, managing large data sets, and optimizing data processing workflows. While familiarity with BigQuery is a plus, we value proficiency in Python, SQL, and ETL skills above all. Key Responsibilities: - ETL Development: Design, develop, and optimize ETL pipelines to efficiently extract, transform, and load large volumes of data into data warehouses.
- Data Warehousing: Create and implement scalable data warehousing solutions by designing and optimizing data models, working with dimensional modeling, and ensuring high performance of data storage systems.
- SQL Querying: Develop advanced SQL queries for data manipulation, transformation, and aggregation. Optimize queries for performance and ensure data integrity.
- Collaboration: Work closely with business analysts and stakeholders to understand data requirements and ensure the delivery of high-quality data.
- Data Processing: Automate data processing tasks and ensure data accuracy, consistency, and security across the data pipelines.
- Documentation and Reporting: Maintain detailed documentation for all data pipelines, data models, and solutions built, and ensure clear reporting of data quality and performance.
- Optimization: Monitor, troubleshoot, and optimize ETL processes, data pipelines, and data models for performance and efficiency.
- Team Collaboration: Work with a global team of engineers, data scientists, and analysts to ensure seamless data integration, accessibility, and insights.
- Support: Provide support for existing ETL systems and help troubleshoot any production issues related to data processing and storage.
Required Skills & Qualifications: - Experience: 8+ years of experience in ETL development, data warehousing, and data pipeline optimization.
- Technical Skills:
- Strong proficiency in Python and SQL.
- Experience working with ETL frameworks and data warehousing technologies.
- Exposure to cloud platforms (preferably Google Cloud Platform, but any cloud platform is acceptable).
- Data Warehousing Knowledge: Expertise in data warehouse architecture, dimensional modeling, and optimizing large-scale data solutions.
- BigQuery Knowledge (Optional): While BigQuery knowledge is not required, any familiarity with BigQuery or cloud-based data warehousing solutions is a plus.
- Problem-Solving: Strong troubleshooting and problem-solving skills, with the ability to address data pipeline bottlenecks and performance issues.
- Certifications: Any relevant certifications related to ETL, data engineering, or cloud platforms would be a plus.
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