Senior Data Engineer

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 GCP, 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