Senior Manager, Data Engineering

Agency: Omnicom Media Job Function: Data and Analytics Job Subfunction: Data Engineering Job Description: This role sits within our Data and Technology team. In this role, you will own and extend our config-driven data platform (DMI), which standardises ingestion, transformation, and delivery of paid media data across 26+ ad platforms for multiple global clients. You will work closely with our team to build and maintain ELT pipelines from Cloud Function ingestion into BigQuery through to dbt-powered transformation ensuring the highest standard in data integrity and scalability. This is an exciting role with excellent career opportunities within a high-profile team and scope to strategically shape the agency. We are looking for someone who can hit the ground running, contribute to a mature mono-repo data platform, and help drive best practices across the engineering team. Experience with digital media data is highly beneficial.

Own and extend the end-to-end data pipeline from Cloud Function ingestion through dbt transformation (staging → intermediate → marts) to analysis-ready tables in BigQuery. Develop and maintain dbt macros, Jinja templates, and platform YAML definitions that auto-generate models across 26+ ad platforms. Manage and improve GCP infrastructure (BigQuery, Cloud Run, Cloud Functions, Cloud Scheduler, Pub/Sub) provisioned via Terraform. Build and maintain the Python CLI tooling that orchestrates client onboarding, config compilation, and pipeline execution. Mentor the team of data engineers, driving best practices in DataOps, code review, testing, and documentation. Proactively review existing processes to identify opportunities to automate manual work, optimise data delivery, and re-design infrastructure for greater scalability. Collaborate with analysts, data scientists, and BI teams (PowerBI, Looker Studio, Tableau, etc.) to maximise the value delivered from data models. Contribute to CI/CD pipelines (Cloud Build), testing (pytest, dbt tests), and documentation (MkDocs, etc.).

Required: Strong experience with dbt - macros, Jinja templating, incremental models, seeds, testing, and packages. Proficient in Python 3.11+  building CLI tools, data processing, and automation. Proficient in SQL, ideally BigQuery dialect. Experience with Google Cloud Platform especially BigQuery, Cloud Run, Cloud Functions, Cloud Storage, Pub/Sub, and Cloud Scheduler Experience with Infrastructure as Code (Terraform) for provisioning and managing cloud resources. Solid understanding of data modelling techniques (star schema, dim/fact architecture, slowly changing dimensions). Comfortable with Git (GitHub, branching strategies, pull requests) and CI/CD (Cloud Build or similar). Ability to translate business needs into technical specifications. Highly Desirable: Experience with Docker and containerised workloads (Cloud Run Jobs). Familiarity with CLI frameworks (Click) and config-driven architectures (Pydantic, YAML-based configuration). Knowledge of the digital media / paid media industry — we process data from 26+ ad platforms (Google Ads, Meta, DV360, TikTok, etc.). Exposure to multi-cloud integrations (Azure Blob, AWS S3, SFTP). Mono-repo experience — managing multi-client configurations in a single codebase.

Experience with Databricks (and dbt-databricks). Familiarity with modern Python dev tooling — Poetry, ruff, mypy, pre-commit. Experience with docs-as-code (MkDocs or similar). Qualities: Ownership – an ability to manage multiple workstreams across clients with accuracy, and see things through from design to deployment. Curiosity – a natural inclination to explore new tools, dig into unfamiliar systems, and understand how things work end-to-end. Resourcefulness – an ability to unblock yourself, whether that means reading source code, querying logs, or finding creative workarounds when data or documentation is limited. Problem-solving – an ability to