11 hours ago
Cairo, EgyptSenior
Responsibilities
- Mentor junior engineers and lead code and design reviews across projects.
- Design, develop, deploy, and manage scalable data pipelines and cloud-native data platforms across GCP and AWS.
- Build ingestion pipelines from marketing, advertising, CRM, and other data sources into cloud data warehouses.
- Architect production-grade data models with dbt or Dataform for reporting, measurement, and ML use cases.
- Write optimized SQL and perform data transformation and performance tuning in cloud data warehouses.
- Design API integrations and automate ETL/ELT processes across cloud environments.
- Own pipeline deployment and orchestration using Cloud Workflows, dbt Cloud, and CI/CD frameworks.
- Apply GenAI tools to accelerate engineering workflows and build client-facing data solutions where relevant.
- Collaborate with stakeholders and clients to understand business challenges and develop technical solutions and roadmaps.
- Evaluate trade-offs among performance, cost, maintainability, and delivery time for data engineering projects.
- Create documentation and establish best practices for pipeline development, data quality, and deployment.
- Communicate complex technical concepts to technical and non-technical stakeholders.
Requirements
- Bachelor's or Master's degree in a quantitative or technical field such as Computer Science, Software Engineering, Data Engineering, or Information Systems.
- At least 3 years of experience in data engineering, analytics engineering, or a related field.
- Experience designing and deploying cloud solutions on GCP or AWS.
- Professional cloud certification such as GCP Professional Data Engineer, GCP Professional Cloud Architect, AWS Data Engineer, AWS Solutions Architect Professional, or equivalent.
- Strong proficiency in SQL and Python.
- Knowledge of data warehousing, ETL/ELT concepts, and modern data stack tools.
- Expertise authenticating and authorizing API requests with OAuth and API keys.
- Expertise with dbt or Dataform and cloud data warehouses such as BigQuery, Snowflake, Redshift, or Databricks.
- Hands-on experience with cloud data services such as Cloud Functions, Cloud Run, Cloud Storage, Pub/Sub, Lambda, S3, or SNS/SQS.
- Experience building and deploying production data pipelines.
- Applied experience using GenAI tools such as Claude Code, Cursor, or Copilot in engineering workflows.
- Familiarity with data privacy principles, regulations, and standards.
- Fluent written and spoken English communication skills.
- Strong analytical, problem-solving, independent-working, and teamwork skills.
Benefits
- Hybrid work arrangement, as indicated by the #LI-Hybrid designation.
- Opportunity to work across the EMEA and MENA regions with Analytics, Strategy, and Data Science teams.
- Inclusive and equal-opportunity workplace committed to diversity, equity, and belonging.
Tech Stack
Amazon RedshiftAWSAzureDatabricksdbtGitHub ActionsGoogle BigQueryGoogle Cloud PlatformPythonSnowflakeSQL
Categories
Data Engineering
About Monks
Monks combines an extraordinary range of global marketing and technology services to accelerate business possibilities and redefine how brands and businesses interact with the world. It delivers unfettered content production, scaled experiences, enterprise-grade technology and data science fueled by AI to help the world’s trailblazing companies outmaneuver and outpace their competition.