# Marketing Data Engineer - Ad Tech (US hours)

**Company:** [VirtuHire](null/companies/kFmPJgXQgJdsTAD6szzoRf.md)
**Location:** Remote
**Workplace:** remote
**Department:** Tech

[Apply for this job](null/view/091fcc84-ae08-4618-aeba-0ae500e842fd)

## Description

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.