Architectural leadership of the enterprise DWH project in an AWS cloud environment. The role focuses on designing robust data infrastructure, establishing the technological roadmap, and enabling seamless large-scale data processing to support BI and AI initiatives.
Key Responsibilities:
Lead the architectural design and implementation of the new enterprise DWH project.
Design, develop, and optimize complex cloud-based data solutions for processing massive datasets.
Select and implement the optimal AWS services (Redshift, Glue, Athena, EMR) and data formats (Parquet, Iceberg) for the core data stack.
Provide technical leadership, mentorship, and professional guidance to the Data Engineering team.
Collaborate closely with development teams, Data Scientists, and end-users to define requirements and deliver innovative Data and AI solutions.
Requirements: Requirements
B.Sc. in Computer Science, Engineering, Mathematics, or Statistics.
Proven experience of several years as a Data Architect, with a strong background as a Data Engineer (focusing on Python and Spark).
Deep expertise in planning and designing AWS cloud environments and utilizing data services: Redshift, Glue, Athena, EMR / EMR Serverless, and RDS.
Proven experience in designing complex data pipelines using Airflow, and microservices architecture based on Docker, ECS, and Kubernetes.
Expertise in large-scale analytical data structures, optimizing partitions, and building Data Lakes / DWH using Parquet and Iceberg.
Experience integrating data solutions with leading BI tools (QuickSight, Tableau, PowerBI) for downstream analytics.
Familiarity with Machine Learning platforms and experience designing architectures that support GenAI / Prompt Engineering solutions.
Proven experience in designing complex data pipelines using Airflow / Step Functions, and microservices architecture based on Docker, ECS, and Kubernetes.
This position is open to all candidates.