The Role
We’re looking for a hands-on Data Engineer to join a growing data team working across AdTech / MarTech.
This role is focused on building and optimising data pipelines and models within Snowflake, working with large-scale marketing and media datasets across multiple platforms.
You’ll play a key role in shaping how data is ingested, transformed, and structured for analytics and activation, working with complex, often messy datasets from across the media ecosystem.
What You’ll Be Doing
- Build and maintain data pipelines into Snowflake using modern ELT practices
- Develop scalable data models and views using dbt
- Ingest and integrate data from multiple marketing platforms (APIs, batch, streaming)
- Work with datasets from DSPs, paid social, and search platforms
- Blend and transform data across multiple sources into unified datasets
- Ensure data quality, integrity, and reliability across pipelines
- Troubleshoot and optimise existing pipelines and workflows
- Support downstream use cases including BI dashboards and analytics
Must-Have Skills
- Strong hands-on experience with Snowflake (building views, tables, data models)
- Proven experience using dbt in production environments
- Advanced SQL skills
- Experience with Python for data processing
- Working knowledge of AWS (S3, Glue, Lambda or similar)
Domain Experience (Critical)
- Background in Media, AdTech, or MarTech environments
- Experience working with data from:
- DSPs (e.g. DV360, The Trade Desk)
- Paid Social (Meta, TikTok, LinkedIn Ads)
- Google Ads
- Strong understanding of how marketing data flows end-to-end
What We’re Looking For
- Experience building end-to-end data pipelines
- Strong exposure to API-based data ingestion
- Experience handling large, messy, and fragmented datasets
- Ability to create clean, structured datasets for reporting and analytics
- Comfortable working in a fast-paced, evolving data environment
Nice to Have
- Experience with Airflow or Dagster
- Working with JSON / semi-structured data
- Familiarity with CI/CD pipelines and Git
Why Join
- Work with high-volume, real-world marketing data across multiple platforms
- Opportunity to own and shape data models and pipelines
- Exposure to modern data stack (Snowflake + dbt + cloud)
- Fast-paced environment with strong growth and learning opportunities
The Role
We’re looking for a hands-on Data Engineer to join a growing data team working across AdTech / MarTech.
This role is focused on building and optimising data pipelines and models within Snowflake, working with large-scale marketing and media datasets across multiple platforms.
You’ll play a key role in shaping how data is ingested, transformed, and structured for analytics and activation, working with complex, often messy datasets from across the media ecosystem.
What You’ll Be Doing
- Build and maintain data pipelines into Snowflake using modern ELT practices
- Develop scalable data models and views using dbt
- Ingest and integrate data from multiple marketing platforms (APIs, batch, streaming)
- Work with datasets from DSPs, paid social, and search platforms
- Blend and transform data across multiple sources into unified datasets
- Ensure data quality, integrity, and reliability across pipelines
- Troubleshoot and optimise existing pipelines and workflows
- Support downstream use cases including BI dashboards and analytics
Must-Have Skills
- Strong hands-on experience with Snowflake (building views, tables, data models)
- Proven experience using dbt in production environments
- Advanced SQL skills
- Experience with Python for data processing
- Working knowledge of AWS (S3, Glue, Lambda or similar)
Domain Experience (Critical)
- Background in Media, AdTech, or MarTech environments
- Experience working with data from:
- DSPs (e.g. DV360, The Trade Desk)
- Paid Social (Meta, TikTok, LinkedIn Ads)
- Google Ads
- Strong understanding of how marketing data flows end-to-end
What We’re Looking For
- Experience building end-to-end data pipelines
- Strong exposure to API-based data ingestion
- Experience handling large, messy, and fragmented datasets
- Ability to create clean, structured datasets for reporting and analytics
- Comfortable working in a fast-paced, evolving data environment
Nice to Have
- Experience with Airflow or Dagster
- Working with JSON / semi-structured data
- Familiarity with CI/CD pipelines and Git
Why Join
- Work with high-volume, real-world marketing data across multiple platforms
- Opportunity to own and shape data models and pipelines
- Exposure to modern data stack (Snowflake + dbt + cloud)
- Fast-paced environment with strong growth and learning opportunities
…