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Marketing Data Engineer - Ad Tech (US hours)

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Remote

Marketing Data Engineer - Ad Tech (US hours)

VirtuHire

Marketing Data Engineer - Ad Tech (US hours)

Skills

SQL QueriesData EngineeringPythonSnowflakeData Analysis

2 months ago

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Important information


Location:

South Africa

Work mode:

Remote

Published on:

26 August 2026

What they need


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.
  • 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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