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About the Role
As a Senior Data Engineer at Monks, you will lead the design, development, and deployment of scalable cloud-native data pipelines and platforms that power marketing analytics, measurement, and activation for our clients. This role requires an engineering expert with proven experience delivering production-grade data solutions on public cloud platforms such as GCP, AWS, or Azure.
You should have strong data literacy with the ability to translate complex client challenges into robust, scalable technical solutions. Ideally, you have a background in marketing or product data engineering, coupled with a deep understanding of modern data stack tools, cloud architecture, and software engineering best practices. 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:
Provide technical mentorship and guidance to junior engineers, and lead code reviews and design reviews across projects.
Upskill the wider team on best practices for cloud data engineering, modern data stack tools, and applied use of AI in engineering workflows.
Design, develop, deploy, and manage scalable data pipelines and cloud-native data platforms across multiple cloud platforms, including GCP and AWS.
Lead the end-to-end delivery of ingestion pipelines from marketing and advertising data sources (e.g. CM360, GA4, Meta, TikTok, CRM systems) into cloud data warehouses.
Architect and implement production-grade data models using dbt or Dataform, turning raw data into analytics-ready datasets that power reporting, measurement, and ML use cases.
Write optimised SQL to transform and serve large, complex datasets in BigQuery, Snowflake, Redshift, or Databricks, leveraging advanced techniques such as window functions, incremental models, and performance tuning.
Design and implement robust API integrations and end-to-end automation of ETL/ELT processes for data integration across cloud environments.
Own the deployment and orchestration of pipelines using tools such as Cloud Workflows, dbt Cloud, and CI/CD frameworks (e.g. GitHub Actions, Cloud Build).
Apply GenAI tools (e.g. Claude, ChatGPT, Gemini) to accelerate engineering workflows and, where relevant, to design and build client-facing data solutions.
Collaborate with internal and external stakeholders to understand their business challenges and support them in building tailored data solutions and technical roadmaps.
Lead the complexities of a data engineering project, demonstrating the ability to evaluate solutions and apply the most suitable approach based on trade-offs between performance, cost, maintainability, and time-to-delivery.
Create detailed documentation and establish best practices for pipeline development, data quality, and deployment to ensure consistency and scalability.
Communicate complex technical concepts and solutions to both technical and non-technical stakeholders, including clients.
Mentor junior engineers through pairing, technical guidance, and career development conversations.
About You
The essentials:
Bachelor's or Master's in a quantitative or…