Senior Data Engineer

at  Saras Analytics

JOB DESCRIPTION

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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. ETL Processes: Proven expertise in designing and implementing Extract, Transform, Load (ETL) processes.
2. SQL and Databases: Proficient in SQL with experience in working with relational databases (e.g., PostgreSQL, MySQL) and cloud-based database services.
3. Data Modeling: Strong understanding of data modeling principles and experience in designing effective data models.
4. Version Control: Familiarity with version control systems, such as Git, for tracking changes in code and configurations.
5. Collaboration Tools: Experience using collaboration and project management tools for effective communication and project tracking.
6. Containerization and Orchestration: Familiarity with containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes).
7. Monitoring and Troubleshooting: Ability to implement monitoring solutions and troubleshoot issues in data pipelines.
8. Data Quality Assurance: Experience in implementing data quality checks and validation processes.
9. 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.

Please refer the Job description for details