AWS Data Architect - Python/Apache Airflow

Role : AWS Data Architect

Job Description

Candidate should possess a deep understanding of big data technologies, cloud services, and data architecture, with a proven track record of leading data-driven projects to successful completion.

Key Responsibilities

• Lead a team of data engineers, providing technical guidance and mentorship.
• Develop and execute a strategic roadmap for data processing, storage, and analytics in alignment with organizational goals.
• Design, implement, and maintain robust data pipelines using Python and Airflow, ensuring efficient data flow and transformation for analytical and operational purposes.
• Utilize AWS services, including S3 for data storage, Glue and EMR for data processing, and orchestrate data workflows that are scalable, reliable, and secure.
• Implement real-time data processing solutions using Kafka, SQS, and Event Bridge, addressing high-volume data ingestion and streaming needs.
• Oversee the integration of diverse systems and data sources through AppFlow, APIs, and other integration tools, ensuring seamless data exchange and connectivity.
• Lead the development of data warehousing solutions, applying best practices in data modelling to support efficient data storage, retrieval, and analysis.
• Continuously monitor, optimize, and troubleshoot data pipelines and infrastructure, ensuring optimal performance and scalability.
• Ensure adherence to data governance, privacy, and security policies, implementing measures to protect sensitive data and comply with regulatory requirements.

Qualification

• Bachelor's or master's degree in computer science, Engineering, or a related field.
• 8-10 years of experience in data engineering, with at least 3 years in a leadership role.
• Proficient in Python programming and experience with Airflow for workflow management.
• Strong expertise in AWS cloud services, particularly in data storage, processing, and analytics (S3, Glue, EMR, etc.).
• Experience with real-time streaming technologies like Kafka, SQS, and Event Bridge.
• Solid understanding of API based integrations and familiarity with integration tools such as AppFlow.
• Deep knowledge of data warehousing concepts, data modelling techniques, and experience in implementing largescale data warehousing solutions.
• Demonstrated ability to lead and mentor technical teams, with excellent communication and project management skills.