Offer summary

Qualifications:

7+ years of experience in building data and analytics platforms in cloud environments., Expertise in data warehousing, lake houses, and streaming solutions such as Azure and Snowflake., Strong background in data modeling techniques like Dimensional and Data Vault., Proficiency in SQL optimization and data engineering tools including Python, Spark, and Kafka..

Key responsabilities:

Collaborate with business and IT teams to optimize the information value chain and provide strategic recommendations.

Design architecture solutions through conceptual and physical models for technology systems.

Lead complex projects, ensuring efficient resource allocation and removal of cross-functional obstacles.

Mentor teams on best practices for data engineering and architectural design to align with business goals.

Job description

As a key member of the Corporate Architecture & Analytics team, the Lead Solutions Architect is focused on advancing the company's technology infrastructure and data solutions, aiming to achieve greater business efficiency and innovation.

Responsabilities

Collaborate with business and IT teams to optimize the information value chain and provide strategic recommendations on data architecture.
Design architecture solutions through conceptual and physical models, visualizing both current and future states of the technology systems.
Lead complex projects, ensuring resources are allocated efficiently and objectives are met by removing cross-functional obstacles.
Act as a data architecture expert, focusing on data orchestration, processing, security, and scalability.
Promote cloud-based technologies and modern data engineering tools across the organization.
Mentor teams on best practices for data engineering and architectural design to align with business goals.
Implement and enforce data modeling standards to ensure high-quality data management practices.
Develop clear communication materials to present strategic initiatives to stakeholders.

Requirements

7+ years of experience building data and analytics platforms in cloud environments.
English advanced
Expertise in data warehousing, lake houses, and streaming solutions (Azure, Snowflake).
Strong background in data modeling (Dimensional, Data Vault).
Proficiency in SQL optimization, data engineering tools (Python, Spark, Kafka, Airflow), and DevOps practices (Github, Docker).
Experience with both relational and NoSQL databases (Oracle, PostgreSQL, MongoDB).

Benefits