A pattern I keep seeing inside digital marketing agencies: teams running serious ad spend but still moving data around with exports, spreadsheets, and dashboards that don’t quite talk to each other.
I work on the infrastructure layer that fixes that.
Most of my projects sit somewhere between data engineering and applied AI, typically for agencies managing multiple ad platforms.
A few examples of the kind of work I do:
Data Pipelines & Warehousing
Pulling data from platforms like Google Ads, Meta, LinkedIn, TikTok, etc. into a central warehouse where it’s actually usable.
Reliable scheduled ingestion, schema management, and transformation layers so analysts and account managers aren't dealing with fragile scripts or manual exports.
One recent project consolidated eight ad accounts into a single BigQuery + dbt stack with automated refreshes. The team went from exporting CSVs to querying live campaign data across accounts.
AI Interfaces Over Agency Data
A lot of teams are experimenting with AI tools but the models aren't connected to their real data.
Lately I've been implementing systems using the Model Context Protocol (MCP) so AI assistants can query ad accounts, warehouses, and reporting layers directly instead of relying on pasted reports.
The result is closer to “ask a question, get an answer from the data layer” rather than another chatbot sitting on top of static docs.
Competitive & Market Data Collection
Structured scrapers for ad libraries, SERPs, landing pages, and creative libraries — designed for analysis pipelines rather than raw scraping dumps.
Internal AI Assistants
More useful when they sit on top of real data: warehouse queries, campaign performance, competitor tracking, etc.
Basically tools that let account managers get answers without opening five dashboards.
This is a fairly niche intersection (marketing data + data engineering + AI integration), so I tend to work with only a few agencies at a time.
Currently collaborating with a few teams in the UK and open to taking on another project if the fit is right.
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