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 a boot strapped, profitable & fast growing (2x y-o-y) startup with old school value systems.

We play in a very exciting space which is intersection of data analytics & ecommerce both of which are game changers.

Today, the global economy faces headwinds forcing companies to downsize, outsource & offshore creating strong tail winds for us.

We are an employee first company valuing talent & encouraging talent and live by those values at all stages of our work without comprising on the value we create for our customers.

We strive to make Saras a career and not a job for talented folks who have chosen to work with us.

The Role :

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 :

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.

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.