Senior Cloud Data Architect

About us:

We are an e-commerce focused end-to-end data analytics firm assisting enterprises and brands in making informed business decisions to maximize value.

Our work spans data extraction, transformation, visualization, and analysis delivered via industry-leading products, solutions, and services. We have developed a proprietary ETL tool called Daton. Additionally, we provide user-friendly data visualization solutions on top of Daton.

Our team comprises experienced professionals who understand the stories behind numbers and communicate these insights effectively to executives.

Today, we are a fast-growing startup with old-school values. We operate in a highly exciting space where data analytics intersects with e-commerce, creating strong tailwinds for our growth. Despite global economic challenges, we remain optimistic about our prospects.

The Role:

We seek a seasoned Senior Data Engineer with substantial experience in cloud technologies to join our data engineering team. As a pivotal member, you will design, implement, and optimize data pipelines to ensure seamless integration with cloud platforms. The ideal candidate will possess expertise in Python, data engineering principles, and successful implementation of scalable solutions in cloud environments.

Responsibilities:

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

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

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 Modeling 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.

If you have the required technical skills and passion for leveraging cloud technologies for data engineering, please submit your resume and cover letter highlighting your technical expertise and relevant experience.