Sr Data Science Engineer

As a Senior Data Science Engineer you will play a critical role in designing developing and maintaining our data infrastructure and pipelines. Your expertise will drive the effective collection storage processing and analysis of largescale data sets enabling actionable insights and datadriven decisionmaking across the organization. You will collaborate with crossfunctional teams including software engineers and product managers to deliver scalable and highperformance data solutions that support our growing product needs.

Job Description in breif

• Design and Develop Data Solutions: Lead the endtoend design and development of scalable efficient and reliable data pipelines and ETL workflows. Implement data integration processes from various sources to central data repositories.
  
• Data Modeling and Architecture: Design and optimize data models for both structured and unstructured data ensuring efficient data storage and retrieval. Evaluate and propose appropriate database technologies and data storage solutions.
  
• Data Quality and Governance: Implement data quality checks and ensure data integrity across all data assets. Establish and maintain data governance practices to guarantee compliance with data security and privacy regulations.
  
• Performance Optimization: Continuously monitor and enhance the performance of data pipelines databases and data processing systems to achieve optimal efficiency and low latency.
  
• Big Data Technologies: Stay uptodate with the latest trends and advancements in big data technologies frameworks and tools. Evaluate and introduce new technologies to improve data engineering processes.
  
• Collaboration and Leadership: Collaborate with data scientists analysts and other stakeholders to understand their data needs and provide the necessary infrastructure and support. Mentor and guide junior data engineers to foster a highperforming data engineering team.
  
• Documentation and Best Practices: Maintain comprehensive documentation for all data processes methodologies and configurations. Promote and enforce best practices in data engineering across the organization.
  
• Troubleshooting and Support: Investigate and resolve datarelated issues including performance bottlenecks data inconsistencies and system failures.
  

Requirements

Requirements:

• Bachelors or master s degree in computer science Software Engineering Data Science or a related field.
  
• Proven experience as a Data Engineer or similar role with at least 4 years of handson experience in building largescale data pipelines and data integration solutions.
  
• Proficient in programming languages such as Python Java Scala or similar with a focus on data manipulation and processing.
  
• Expertise in working with big data technologies like Bigquery Redshift Snowflak Spark Kafka or similar distributed computing frameworks.
  
• Strong experience with SQL and NoSQL databases data warehousing and data modeling techniques.
  
• Indepth knowledge of cloud platforms like AWS Azure or Google Cloud and experience with cloudbased data storage and processing services.
  
• Familiarity with data security and privacy standards as well as data governance practices.
  
• Excellent problemsolving skills and the ability to analyze complex datarelated issues.
  
• Strong communication skills to collaborate effectively with crossfunctional teams and stakeholders.
  
• Experience with containerization technologies (e.g. Docker Kubernetes) is a plus.
  
• Certifications in relevant data engineering or cloud technologies are advantageous.
  

Automation Strategy: Develop and execute the automation strategy, providing leadership in the selection and implementation of automation frameworks. Collaborate with stakeholders to align automation efforts with overall testing and development goals. Framework Design and Enhancement: Design, enhance, and maintain scalable and modular automation frameworks for Selenium, Appium, and API testing. Lead efforts to implement best practices in test automation, ensuring efficiency, reliability, and maintainability. Test Automation: Lead the development and execution of automated test scripts using Selenium, Appium, and API testing tools. Provide technical guidance to the automation team and ensure the coverage of critical test scenarios. Collaboration and Mentoring: Collaborate with cross-functional teams, including developers, QA engineers, and product managers, to drive automation initiatives. Mentor and provide technical leadership to automation engineers, fostering skill development and knowledge sharing. Quality Assurance Best Practices: Drive the implementation of best practices in quality assurance, ensuring adherence to industry standards and regulations. Conduct regular reviews of automation processes and provide recommendations for improvement. Continuous Integration and Deployment (CI/CD): Integrate automated tests into CI/CD pipelines to enable continuous testing and delivery. Collaborate with DevOps teams to enhance automation in the deployment process. Defect Management: Lead defect triage meetings, providing technical insights into identified issues. Work closely with development teams to prioritize and facilitate the resolution of defects. Training and Development: Conduct training sessions for the QA team and other stakeholders on automation best practices, tools, and technologies. Stay informed about emerging trends in automation and provide recommendations for skill development.