Senior Data Engineer
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 technical subject such as Computer Science, Software Engineering, Data Engineering, Information Systems, or a related field.
3+ years of working experience in data engineering, analytics engineering, or a related field.
Experience in designing and deploying cloud solutions on platforms such as 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.
Solid understanding of data warehousing, ETL/ELT concepts, and modern data stack tools.
Expertise in authenticating and authorising API requests using methods such as OAuth and API keys, and troubleshooting common API-related challenges.
Expertise with data modelling tools such as dbt or Dataform.
Expertise with cloud data warehousing platforms such as BigQuery, Snowflake, Redshift, or Databricks.
Hands-on experience with core cloud data services such as Cloud Functions, Cloud Run, Cloud Storage, Pub/Sub, or their AWS equivalents (Lambda, S3, SNS/SQS).
Experience building and deploying data pipelines in production environments.
Applied experience using GenAI tools in engineering workflows (e.g. Claude Code, Cursor, Copilot).
Familiarity with data privacy principles and best practices, including compliance with relevant regulations and standards.
Fluent English communication and written skills.
Strong analytical and problem-solving skills with the ability to work independently and in a team environment.
At Monks, we believe in fostering an environment where a diversity of perspectives can thrive. We proactively work to design hiring processes that promote equity and inclusion while mitigating bias. We celebrate diversity and are committed to building a team that reflects the communities we serve. We welcome and encourage qualified applicants from all backgrounds who are excited to contribute to our mission.
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