Data Engineer

Role: Data Engineer

Location

Remote Work Week: Sunday Thursday Work Timings: 9:00 AM 6:00 PM (Saudi Arabian Time Zone) Overview We re seeking a Data Engineer to design, build, and maintain the data infrastructure that underpins our analytics, ML models, and decision-making processes. You ll be responsible for building scalable data pipelines, integrating diverse data sources, and ensuring data quality, reliability, and accessibility across the organization. Working closely with data scientists, analysts, and product teams, you ll enable data-driven insights while optimizing for performance and scalability. This is a great opportunity to have a direct impact on how data is leveraged across a fast-growing company. Roles & Responsibilities Design, build, and maintain scalable and reliable data pipelines to support analytics, ML models, and business reporting. Collaborate with data scientists and analysts to ensure data is available, clean, and optimized for downstream use. Implement data quality checks, monitoring, and validation processes. Design efficient ETL/ELT workflows and integrate multiple data sources (databases, APIs, third-party tools) into centralized storage (data lakes/warehouses). Support cloud-based infrastructure for data storage and retrieval. Monitor, troubleshoot, and optimize existing pipelines for large-scale, real-time data flows. Apply best practices for query optimization and cost-efficient storage. Partner with product, engineering, and business stakeholders to gather requirements. Document workflows, schemas, and processes while supporting a culture of reliability, governance, and security.

Requirements

• Proficiency in Python and SQL for data engineering tasks
• Strong understanding of ETL/ELT processes, data warehousing, and data modeling
• Hands-on experience with cloud platforms (AWS, GCP, or Azure) and data storage solutions (BigQuery, Redshift, Snowflake, etc.)
• Familiarity with data orchestration tools Airflow, Airbyte is a must
• Experience with containerization & deployment tools (Docker, Kubernetes) is a plus
• Knowledge of data governance, security, and best practices for handling sensitive data.Familiarity to work with Git and GitHub
• Dataform is a must Strong skills in eliciting requirements from cross-functional stakeholders and translating them into actionable data engineering tasks

Experience

Preferably total 4 years of experience 2+ years in data engineering, building and maintaining data pipelines. 2+ years of SQL and Python development in production environments. Experience working in Fast-growing startup environments is a plus. Exposure to real-time data processing frameworks (Kafka, Spark, Flink) is a plus. Why Join Soum: Be part of a fast-scaling Saudi marketplace where Finance has real influence over how the business is run. Work directly with senior Finance leadership and gain broad exposure across accounting, audit, and financial operations. A culture that values ownership, speed, and constant improvement over hierarchy and red tape. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Desired Candidate Profile

Proficiency in Python and SQL for data engineering tasks. Strong understanding of ETL/ELT processes, data warehousing, and data modeling. Hands-on experience with cloud platforms (AWS, GCP, or Azure) and data storage solutions (BigQuery, Redshift, Snowflake, etc.). Familiarity with data orchestration tools Airflow, Airbyte is a must. Experience with containerization & deployment tools (Docker, Kubernetes) is a plus. Knowledge of data governance, security, and best practices for handling sensitive data.Familiarity to work with Git and GitHub. Dataform is a must Strong skills in eliciting requirements from cross-functional stakeholders and translating them into actionable data engineering tasks.

Experience

Preferably total 4 years of experience 2+ years in data engineering, building and maintaining data pipelines. 2+ years of SQL and Python development in production environments. Experience working in Fast-growing startup environments is a plus. Exposure to real-time data processing frameworks (Kafka, Spark, Flink) is a plus.