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:
1. 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.
2. 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.
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 cost-effectiveness 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 cloud-based data warehouse.
Collaborate with cross-functional 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 cloud-based 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 hands-on experience with workflow management tools like Apache Airflow.
3. 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.
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 cloud-based 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 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.
