Senior Data Engineering Specialist

About Us:

We are a cutting-edge data analytics firm focused on delivering innovative solutions to businesses. Our team of experts provides end-to-end data services, from extraction and transformation to visualization and analysis. We utilize industry-leading tools and technologies to ensure seamless integration and maximum business value.

Our flagship product is an ETL tool designed to streamline data processes. We have also developed user-friendly data visualization solutions that cater to the needs of various industries.

Where We Stand:

We are a rapidly growing startup with a strong focus on talent development and employee satisfaction. We operate in a dynamic space where data analytics and e-commerce intersect, creating exciting opportunities for growth and innovation.

The Role:

We are seeking a seasoned Senior Python Data Engineer to join our team. As a pivotal member, you will play a crucial role in designing, implementing, and optimizing data pipelines using cloud-based technologies. The ideal candidate will possess a strong command of Python, data engineering principles, and experience in cloud environments.

Data Pipeline Development:

Design, develop, and maintain scalable and efficient data pipelines using Python and cloud-based technologies.

Implement Extract, Transform, Load (ETL) processes to seamlessly move data from diverse sources into our cloud-based data warehouse.

Cloud Integration:

Utilize cloud platforms (e.g., Google Cloud, AWS, Azure) to deploy, manage, and optimize data engineering solutions.

Leverage cloud-native services for storage, processing, and analysis of large datasets.

Data Modelling and Architecture:

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.

Optimization and Performance:

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.

Quality Assurance:

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.

Collaboration and Communication:

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.

Documentation:

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

Technical Skills:

• Programming Languages: Proficiency in Python for data engineering tasks, scripting, and automation.
• Data Engineering Technologies: Extensive experience with data engineering frameworks like distributed data processing.
• Understanding and hands-on experience with workflow management tools like Apache Airflow.
• Cloud Platforms: 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.

Soft Skills:

• 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.

We encourage applicants who possess the required technical skills and passion for leveraging cloud technologies for data engineering to apply. Please submit your resume and a cover letter highlighting your technical expertise and relevant experience.