Saras Analytics
Location
Hyderabad | India
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. 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.Job tags
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