Data Engineer

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About the Role

As a Data Engineer at Monks, you will design, build, and deploy cloud-native data pipelines and platforms that power marketing analytics, measurement, and activation for our clients. You will collaborate closely with senior engineers, data scientists, and strategists to deliver scalable data solutions on public cloud platforms such as GCP, AWS, or Azure.

You should have a strong foundation in data engineering fundamentals, SQL, cloud data warehousing, and pipeline development with the ability to translate client requirements into robust technical solutions. Ideally, you have some experience working with marketing or product data, coupled with a solid grounding in modern data stack tools (dbt, BigQuery, orchestration frameworks) and cloud technologies. While the role focuses on data engineering, it also offers opportunities to contribute to data science and GenAI-powered projects.

This role spans across the EMEA and MENA regions and involves close collaboration with our Analytics, Strategy, and Data Science teams. We offer a dynamic and challenging work environment at the forefront of AI-driven innovation in marketing. If you're passionate about building the data foundations that power modern digital experiences, we encourage you to apply!

Responsibilities

• Assist in designing, building, and deploying data pipelines and cloud-native data solutions on platforms such as GCP and AWS.
• Support the development of ingestion pipelines from marketing and advertising data sources (e.g. CM360, GA4, Meta, TikTok, CRM systems) into cloud data warehouses.
• Contribute to the design and implementation of data models using dbt or Dataform, turning raw data into analytics-ready datasets that power reporting, measurement, and ML use cases.
• Write performant SQL to transform and serve large datasets in BigQuery, Snowflake, Redshift, or Databricks.
• Support API authentication and automation of ETL/ELT processes for data integration across cloud environments.
• Contribute to the deployment and orchestration of pipelines using tools such as Cloud Workflows or dbt Cloud.
• Help apply GenAI tools (e.g. Claude, ChatGPT, Gemini) to accelerate engineering workflows and, where relevant, to client-facing data solutions.
• Learn to navigate the lifecycle of a data engineering project, understanding trade-offs between performance, cost, maintainability, and time-to-delivery.
• Follow and contribute to documentation and best practices for pipeline development, data quality, and deployment to ensure consistency and scalability.
• Communicate technical concepts and solutions to both technical and non-technical stakeholders, including clients.

About You

The essentials

• Bachelor's or Master's in a quantitative or technical subject such as Computer Science, Software Engineering, Data Engineering, Information Systems, or a related field.
• 1+ years of working experience in data engineering, analytics engineering, or a related field.
• Familiarity with designing and deploying cloud solutions on platforms such as GCP or AWS.
• Associate Cloud Certification such as GCP Associate Cloud Engineer, GCP Professional Data Engineer, AWS Solutions Architect Associate, or equivalent.
• Strong proficiency in SQL and Python.
• Solid understanding of data warehousing, ETL/ELT concepts, and modern data stack tools (e.g. dbt, BigQuery, Snowflake).
• Good understanding of APIs, including authentication methods (e.g. API keys, OAuth), with a curiosity to explore and troubleshoot.
• Familiarity with core cloud data services such as Cloud Functions, Cloud Run, Cloud Storage, Pub/Sub, or their AWS equivalents (Lambda, S3, SNS/SQS).