Data Engineer Jobs

About The Position

Requirements

• 3–5 years of professional data engineering experience, including production deployments on a major cloud data warehouse such as BigQuery, Redshift, or Snowflake.
• Strong SQL skills and solid Python (or equivalent) for pipeline development and transformation logic.
• Demonstrated experience working with advertising platform data, pulling from platform APIs or connectors (e.g., Google Ads, GA4, Meta, TikTok, DV360) and reconciling it against platform reporting.
• Understanding of advertising data concepts: impressions, clicks, conversions, attribution windows, UTM taxonomy, audience and cost data.
• Experience with a workflow orchestrator (Airflow, Dagster, Prefect, or similar) and version control (Git).
• Comfort working directly with non-technical stakeholders (media planners, analysts, and account leads) to scope and deliver.

Nice To Haves

• Experience with dbt for transformation and modelling.
• Familiarity with Looker Studio, PowerBI, or similar for downstream BI enablement.
• Exposure to marketing measurement work such as MMM, MTA, incrementality testing, or clean-room environments (Ads Data Hub, Meta Advanced Analytics, Amazon Marketing Cloud).
• Experience with broader cloud platform services such as GCP (Cloud Functions, Pub/Sub, Dataflow), AWS (Lambda, S3, Glue), or Azure equivalents.
• Exposure to Databricks or other lakehouse platforms for large-scale data processing.
• Prior experience at a media agency, ad-tech vendor, or in-house marketing data team.

Responsibilities

• Design, build, and maintain production data pipelines in a cloud data warehouse — BigQuery, Redshift, or Snowflake — from ingestion through to analytics-ready models.
• Develop and manage API and connector-based integrations with major advertising platforms: Google Ads, GA4, Meta, TikTok, DV360, Campaign Manager 360, LinkedIn Ads, and similar.
• Model campaign, spend, and performance data into clean, well-documented datasets that media, analytics, and client-services teams can trust.
• Monitor pipeline health inclusive of enabling taxonomy compliance, troubleshoot data discrepancies against platform UIs, and own the fix end-to-end.
• Partner with analysts, strategists, and ad-ops to translate reporting and measurement needs into scalable data models.
• Contribute to data quality, documentation, and engineering standards across the team.

Benefits

• A range of medical, dental, RRSP, paid time off, and/or other benefits also are available to all permanent employees.