Marketing Data Engineer - Ad Tech (US hours)
At VirtuHire · South Africa
- Data and ML
Where you can work from
Open to South Africa
“South Africa”
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- Posted
- 26 August 2026
- First seen by us
- 8 October 2026
- Last checked
- 7 hours ago
Job description
From VirtuHire’s job post, as they wrote it.
Our client in the US is looking for a Marketing Data Engineer to own the data pipelines and data-quality foundation that power their dashboards, pacing outputs and recurring reporting.
Core responsibilities
* Build and maintain API/ETL ingestion from DSPs, ad servers and other advertising platforms.
* Normalize campaign data and maintain metric definitions used across dashboards and reporting.
* Manage cloud-warehouse structures and client data deliveries where required.
Read the full description
* Reconcile pipeline output against raw platform exports and investigate material variance.
* Build automated data-quality checks, alerting and monitoring for pipeline/dashboard health.
* Manage service-account/API credential workflows in the clients owned environments.
* Support platform migrations and rebuild data integrations without reporting discontinuity.
* Partner with Dashboard Developer and Programmatic Lead on definitions, mapping and release validation.
Requirements
Must-have profile
* 4+ years in data engineering/analytics engineering with production ETL/API responsibility.
* Strong SQL plus at least one production programming/scripting language such as Python.
* Experience with cloud data warehouses such as BigQuery, Snowflake or equivalent.
* Experience reconciling data across multiple source systems and designing data-quality controls.
* Ability to own production pipelines, troubleshoot failures and document data definitions clearly.
Preferred experience
* Direct experience with advertising/marketing platform APIs and campaign data.
* Experience with DV360 or other DSP data models, ad-server data or marketing attribution feeds.
* Experience with scheduled reporting/alerting and secure client data delivery.
What success looks like
* Reporting data is reliable and reconciles within agreed tolerance.
* Pipeline failures or data lag are detected before clients notice.
* Platform changes do not create reporting gaps.
* Metric definitions remain consistent across dashboards and recurring reports.
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