Datab

• Job Description
• Datab
• Contractor
• Remote

Job Summary

We are searching for a seasoned Databricks Architect to join our team and spearhead transformative data initiatives. As an expert in big data architecture and data streaming, you will play a pivotal role in designing, implementing, and optimizing scalable data solutions for real-time processing on cloud platforms.

Key Responsibilities

• Architect, design, and implement scalable end-to-end data solutions using the Databricks platform.
• Lead the development of real-time data streaming pipelines utilizing Apache Spark Structured Streaming and Delta Live Tables.
• Collaborate with business and technical stakeholders to gather requirements and translate them into robust technical solutions.
• Provide expert consulting and advisory support on data architecture, cloud migrations, and modern platform integrations.
• Integrate Databricks solutions with major cloud platforms (AWS, Azure, GCP) according to project needs.
• Optimize big data workflows for enhanced performance, cost efficiency, and scalability.
• Mentor and guide development teams in Databricks, real-time frameworks, and best engineering practices.

Required Skills and Qualifications

• 10+ years of experience in Data Engineering, Big Data, or Data Architecture roles.
• Demonstrated hands-on expertise with Databricks, including advanced platform capabilities.
• Strong proficiency in streaming technologies such as Apache Spark Streaming, Structured Streaming, and Kafka integration.
• Proven track record in designing data pipelines, ETL/ELT processes, and real-time analytics solutions.
• Advanced programming skills in Python, Scala, and SQL.
• Experience delivering data solutions within cloud environments (AWS, Azure, GCP).
• Exceptional written and verbal communication abilities for effective stakeholder management.
• Mandatory experience working on projects in the GCC region.

Preferred Qualifications

• Previous experience in consulting or client-facing architectural roles.
• In-depth understanding of data modeling, data lakes, and lakehouse architectures.
• Exposure to technical governance and architecture review processes.