Semi Senior Data Engineer

About us

Monks is a digital-first marketing and advertising services company connecting the dots across content, data & digital media and technology services. Inspired by the connectivity and flexibility of technology APIs, Monks’ single-P&L model offers brands seamless access to a nearly 6,000-strong team of digital talent organized across 57 talent hubs in 33 countries. 

With us, you'll find a diverse group of colleagues with different backgrounds and perspectives. We believe everyone has something of value to offer, and that sustaining a truly diverse, equitable and inclusive workplace begins with fostering an environment where people can be themselves, authentically, every day. We want to build something with the potential to change the heart of our industry, and we’d love to include your unique perspective.

About the role

The Semi Senior Data Engineer will help design, build, and evolve scalable data solutions for digital marketing and advertising use cases. Working closely with our Analytics and Solutions Engineering teams, you will be responsible for end-to-end data engineering activities—from data discovery and ingestion to transformation, orchestration, and deployment—supporting reporting, measurement, and analytics use cases.

This role is centered on developing robust data pipelines and analytics-ready data models across multiple platforms (DSPs, ad servers, and third-party sources). You will also contribute to client project phases, including requirements clarification, task planning and estimation, and solution recommendations.

In addition, the role involves ensuring data quality, reconciliation across heterogeneous datasets, and supporting measurement workflows such as cross-platform metric allocation and incrementality/attribution reporting.

Tools

We are looking for someone experienced with (or familiar with) the following tools and technologies:

• Strong programming skills in SQL, Python, or other programming languages
• Experience with SQL and NoSQL databases
• Cloud platforms such as Databricks, AWS, Azure, or Google Cloud Platform
• Strong knowledge of cloud infrastructure, e.g. on GCP: BigQuery, Cloud Storage, Cloud Run Jobs, Cloud Monitoring, Dataflow, BigQuery Data Transfer Service, Storage Transfer Service
• Data modeling and transformation tools, such as dbt (including for BigQuery)
• Terraform (infrastructure as code)
• GitHub and GitHub Actions (CI/CD processes)
• Looker and LookML is a plus
• JavaScript knowledge is a plus
• Knowledge of media reporting UIs / APIs (e.g., DV360, SA360, Meta) is a plus

What we’re looking for

• A Bachelor’s or Master’s degree in informative engineering, computer science, applied mathematics, engineering, or a related quantitative field—or equivalent work experience.
• 2-3 years of experience with any combination of analytics, marketing analytics, and analytical techniques for marketing, customer, and business applications.
• Hands-on experience with SQL and Python, along with experience using cloud products.
• Hands-on experience developing data pipelines using common ETL/ELT tools and building data architecture patterns.
• Familiarity with Agile methodologies and applying DataOps practices to build and deliver pipelines reliably.
• Experience working with media/advertising data is a plus
• Demonstrated ability to operate effectively independently and within a team

Responsibilities

• Design, build, and maintain dbt models that integrate marketing and advertising data across DSPs, ad servers, and third-party platforms—reconciling differences in granularity (e.g., placement-level vs. creative-level metrics) and resolving join-key quality issues.
• Develop and maintain business logic for cross-platform metric allocation (e.g., distributing spend and impressions across creatives) to support monthly reporting and incrementality/attribution use cases.
• Build and maintain ingestion pipelines from diverse external sources, including cloud-to-cloud transfers (e.g., AWS S3 → GCS), Azure/SFTP drops, and vendor APIs, ensuring deduplication and data quality.
• Own and evolve GCP cloud infrastructure using Terraform, following established modular patterns and naming conventions; partner with platform teams on environment and deployment standards.
• Lead the migration of legacy ingestion patterns (e.g., BigQuery Data Transfers) to scalable, maintainable solutions built on Dataflow and Cloud Run.
• Configure and maintain observability and alerting across the stack (e.g., dbt source freshness, Cloud Run job failures, BigQuery Data Transfer issues) using Cloud Monitoring, deployed via Terraform.
• Manage the release lifecycle across QA, STG, and PROD environments, including branching strategy, scheduled releases, and automated release-note generation via GitHub Actions.
• Produce and maintain high-quality technical documentation, runbooks, and design artifacts; review and curate client-facing release communications.
• Collaborate with analytics, measurement, and client-facing teams to translate reporting and measurement requirements into reliable data products.

#LI-Hybrid #LI-VG1#LI-AS1 #ID: 12409