Senior Data Engineer

· Collaborate with data scientists, analysts, and other stakeholders to design effective data models that align with business requirements.

· Ensure the scalability, reliability, and performance of the overall data infrastructure on cloud platforms.

· Continuously optimize data processes for improved performance, scalability, and cost-effectiveness in a cloud environment.

· Monitor and troubleshoot issues, ensuring timely resolution and minimal impact on data availability.

· Implement data quality checks and validation processes to ensure the accuracy and completeness of data in the cloud-based data warehouse.

· Collaborate with cross-functional teams to identify and address data quality issues.

· Work closely with data scientists, analysts, and other teams to understand data requirements and provide technical support.

· Collaborate with other engineering teams to seamlessly integrate data engineering solutions into larger cloud-based systems.

· Create and maintain comprehensive documentation for data engineering processes, cloud architecture, and pipelines.

Programming Languages: Proficiency in Python for data engineering tasks, scripting, and automation.

· Extensive experience with data engineering frameworks like distributed data processing.

· Understanding and hands-on experience with workflow management tools like Apache Airflow.

· In-depth knowledge and hands-on experience with at least one major cloud platform: AWS, Azure, or Google Cloud.

· Familiarity with cloud-native services for data processing, storage, and analytics.

ETL Processes: Proven expertise in designing and implementing Extract, Transform, Load (ETL) processes.

SQL and Databases: Proficient in SQL with experience in working with relational databases (e.g., PostgreSQL, MySQL) and cloud-based database services.

Data Modeling: Strong understanding of data modeling principles and experience in designing effective data models.

Version Control: Familiarity with version control systems, such as Git, for tracking changes in code and configurations.

Collaboration Tools: Experience using collaboration and project management tools for effective communication and project tracking.

Containerization and Orchestration: Familiarity with containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes).

Monitoring and Troubleshooting: Ability to implement monitoring solutions and troubleshoot issues in data pipelines.

Data Quality Assurance: Experience in implementing data quality checks and validation processes.

Agile Methodologies: Familiarity with agile development methodologies and practices.

• Strong problem-solving and critical-thinking abilities.
• Excellent communication skills, both written and verbal.
• Ability to work collaboratively in a cross-functional team environment.
• Attention to detail and commitment to delivering high-quality solutions.

If you possess the required technical skills and are passionate about leveraging cloud technologies for data engineering, we encourage you to apply.

Please submit your resume and a cover letter highlighting your technical expertise and relevant experience.