Senior Python Data Engineer

About Saras Analytics:

We are an ecommerce focused end to end data analytics firm assisting enterprises & brands in data driven decision making to maximize business value. Our suite of work spans extraction transformation visualization & analysis of data delivered via industry leading products solutions & services. Our flagship product is Daton an ETL tool. We have now ventured into building exciting ease of use data visualization solutions on top of Daton. And lastly we have a world class data team which understands the story the numbers are telling and articulates the same to CXOs thereby creating value.

Where we are Today:

We are seeking a seasoned and proficient Senior Python Data Engineer with substantial experience in cloud technologies. As a pivotal member of our data engineering team you will play a crucial role in designing implementing and optimizing data pipelines ensuring seamless integration with cloud platforms. The ideal candidate will possess a strong command of Python data engineering principles and a proven track record of successful implementation of scalable solutions in cloud environments.

Responsibilities:

1. Data Pipeline Development:

Design develop and maintain scalable and efficient data pipelines using Python and cloudbased technologies.

2. Cloud Integration:

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

Leverage cloudnative services for storage processing and analysis of large datasets.

3. 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.

4. Optimization and Performance:

Continuously optimize data processes for improved performance scalability and costeffectiveness in a cloud environment.

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

5. Quality Assurance:

Implement data quality checks and validation processes to ensure the accuracy and completeness of data in the cloudbased data warehouse.

Collaborate with crossfunctional teams to identify and address data quality issues.

6. 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 cloudbased systems.

7. Documentation:

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

Technical Skills:

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

2. Data Engineering Technologies:

Extensive experience with data engineering frameworks like distributed data processing.

Understanding and handson experience with workflow management tools like Apache Airflow.

3. Cloud Platforms:

Indepth knowledge and handson experience with at least one major cloud platform: AWS Azure or Google Cloud.

Familiarity with cloudnative services for data processing storage and analytics.

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

5. SQL and Databases: Proficient in SQL with experience in working with relational databases (e.g. PostgreSQL MySQL) and cloudbased database services.

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

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

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

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

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

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

12. Agile Methodologies: Familiarity with agile development methodologies and practices.

Soft Skills:

• Strong problemsolving and criticalthinking abilities.
  
• Excellent communication skills both written and verbal.
  
• Ability to work collaboratively in a crossfunctional team environment.
  
• Attention to detail and commitment to delivering highquality 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.