Senior Commercial Data Engineer

dmg::media is home to some of the world s most recognised news brands and processes billions of data points every month to power audience insights, targeting strategies, and data-driven decision-making across the business. With an ambition to become the world s leading publisher in the sophisticated application of data, dmg::media continues to invest heavily in modern data technologies and scalable analytics platforms.

As a Senior Commercial Data Engineer, you will be central to building a new commercial data solution for programmatic advertising reporting, as part of dmg::media s next-generation data platform. You will design, develop, and maintain scalable data pipelines, ETL processes, real-time data streams, APIs, and insight tools delivering data products that drive measurable impact across both commercial and editorial functions. Working in a highly collaborative engineering environment, you will partner closely with data analysts, data scientists, and commercial stakeholders to build robust data solutions, optimise data architecture, and raise the quality bar for data products across the organisation.

Roles and Responsibilities:

• Apply commercial and programmatic advertising context - including the ad-serving and exchange ecosystem (ad servers, SSPs, DSPs) - to build and support a programmatic advertising reporting solution.

• Design, plan, and implement scalable data pipelines for programmatic advertising and commercial reporting, working across event-driven, scheduled, and streaming data flows.

• Manage and optimise data pipelines, including pre-processing, cleaning, and transforming structured and unstructured data.

• Design and maintain data solutions within a Medallion data architecture (Bronze/Silver/Gold).

• Work with relational databases, applying strong data modelling and a solid data engineering background across the data estate.

• Partner closely with commercial stakeholders on requirement gathering, ensuring data solutions align with commercial and business objectives.

• Work with large, complex datasets and data architectures spanning multi-cloud environments.

• Own data QA and reconciliation between data pipeline processes and downstream interfaces or result sets.

• Maintain config-driven partner-to-brand mapping and related configuration structures alongside commercial stakeholders.

• Apply strong data modelling practices and collaborate with the BI/BIU team to support efficient, accurate reporting.

  Key Requirements:

• Proven commercial experience in programmatic advertising/ad tech , with hands-on exposure to the ad-serving and exchange ecosystem, including ad servers, SSPs, and demand-side partners (e.g., Google Ad Manager, Magnite, Index Exchange, or similar).

• Experience applying commercial and business context to develop and support a programmatic advertising reporting solution , including data ingestion, QA between ingestion and interface result sets, and config-driven partner-to-brand mapping alongside commercial stakeholders.

• Experience working within a news or media publisher is preferred, bringing familiarity with the commercial and editorial context of the business.

• 5+ years of hands-on experience as a Data Engineer, designing and building scalable data pipelines across event-driven, scheduled, and streaming architectures.

• Strong hands-on expertise in Python and SQL is mandatory and is the core technical requirement for this role - including data transformation, performance optimisation, data modelling, and workflow automation - regardless of the specific cloud environment or tool stack used previously.

• Hands-on experience working within a Medallion Architecture (Bronze/Silver/Gold) , and broader familiarity with Data Lake, Data Warehouse, and Lakehouse patterns.

• Proven experience developing and maintaining ETL/ELT pipelines, including ingestion from APIs, databases, cloud storage platforms, and third-party data sources.

• Strong relational database background, including complex SQL development, data modelling, and query optimisation across platforms such as PostgreSQL, SQL Server, and Oracle.

• Experience working across multi-cloud environments (e.g., GCP, AWS, Azure ) is preferred but not essential.

• Hands-on experience with Databricks, Python DAGs, PySpark/Spark DataFrames, and orchestration tools such as Apache Airflow is preferred but not essential - the core requirement is strong Python and SQL, not a specific tool.

• Experience with version control and CI/CD (e.g., GitHub Actions, GitLab CI/CD, Azure DevOps) and designing reusable Python libraries and frameworks for data engineering workflows.

• Strong data modelling skills , with experience supporting BI/reporting teams and tools (e.g., Power BI, Looker, Tableau, or similar) are preferred.

• Strong problem-solving skills, able to independently analyse business requirements and collaborate effectively with analysts and stakeholders; exposure to containerization (Docker/Kubernetes) is preferred.

  Special Requirements for the Role

• Be a self-starter, a problem solver, have a positive attitude, and have great attention to detail.

• Comfortable operating independently in environments without a large support organisation.

• Strong English communication skills, both written and spoken.

• Strong organizational, interpersonal, team-player, problem-solving, and analytical skills.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.