Data Engineer- Python, apache airflow 6+ yrs

Company Description

Funic Tech is a leading business and technology solutions provider, delivering comprehensive and innovative solutions to clients worldwide. We specialize in accelerating digital transformation through expert consulting, cutting-edge technology, and tailored business strategies. Our services range from designing and developing new solutions to managing, enhancing, and supporting existing business systems. With a team of passionate professionals, we are committed to creating world-class products and services that empower businesses to stay ahead of the competition.

Role Description

This is a full-time hybrid role for a Data Engineer specializing in Python and Apache Airflow, requiring over 6 years of relevant experience. The position is based in Pune, with flexibility to work from home on certain days. The role involves designing, building, and maintaining data pipelines, implementing ETL processes, managing large-scale data warehouses, and supporting data analytics initiatives. Additionally, the candidate will collaborate with cross-functional teams to ensure optimized data workflows and efficient data management.

Qualifications

• Proficiency in Data Engineering, including building scalable data pipelines and optimizing data processing workflows
• Strong skills in Data Modeling and Data Warehousing to ensure accessible and well-structured data for analytics
• Experience with Extract Transform Load (ETL) processes and frameworks for efficient data integration
• Understanding of Data Analytics concepts and tools to extract meaningful insights from complex datasets
• Expertise in Python and Apache Airflow for automation and orchestration of workflows
• Ability to work in a hybrid environment with strong communication and collaboration skills
• Proactive problem solver with experience in cloud platforms (AWS, Azure, or GCP) is highly desirable
• Bachelor’s degree in Computer Science, Engineering, or a related field is required; a Master's degree is preferred