Marketing Data Engineer - Ad Tech (US hours)
Summary
The Marketing Data Engineer will build and maintain ETL pipelines and data-quality controls for advertising platforms using SQL, Python, and cloud data warehouses. This role ensures the reliability of marketing dashboards and reporting by reconciling data across various ad-tech sources.
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.
Skills
As published by workable · 2 questions
Basics
First name, Last name, Email, Headline, Phone, Address, Photo, What city and country are you currently based in? (Answer as: City, Country), How many years of experience do you have in the role you are applying to?, Have you previously worked for a UK/US company?, Education, Experience, Summary, Resume, Do you have a degree/diploma?, Cover letter, What is your desired salary? (In ZAR), What is your current notice period?
Pick from a list (2)
- Are you currently based in South Africa?
- Are you willing and able to work US hours?