Data Science Team Lead

Global Remote Role: Data Engineering Lead (Marketing)

We're an early-stage startup that has found Product Market Fit , and we're excited to offer this critical role at Seeda as we work to add 1,000 customers to our industry-leading MMM platform. This is a career-defining opportunity for the right person to help us bring our cutting-edge AI product to marketers globally.

We help brands understand what's actually driving their marketing performance — cutting through attribution chaos to deliver clear, actionable insights on marketing effectiveness.
But here's the reality: before any model can run, someone needs to wrestle each client's fragmented, messy marketing data into shape.

Right now, our senior marketing scientists and modelling experts are being dragged into data wrangling. They're spending their time contextualising and blending client data instead of doing what they're brilliant at — building models and delivering strategic insights.
Meanwhile, we have strong data engineers who can build pipelines, but they lack the marketing domain expertise to know what the data means and how it should be structured for MMM.
We need someone who can bridge this gap: a senior technical leader who deeply understands both the engineering and the marketing data context required to deliver model-ready datasets.

As Marketing Data Engineering Lead, you'll own the critical layer between raw client data chaos and clean model inputs. You'll work across multiple clients, building scalable frameworks that transform fragmented marketing data into reliable, contextualised datasets.
Design and build robust data pipelines that ingest, validate, transform, and blend marketing data from dozens of sources (paid media platforms, ad servers, CRMs, GA4, offline channels, and more)
Establish data models and schemas purpose-built for Marketing Mix Modelling — you understand what MMM needs (spend, impressions, GRPs, conversions, external factors) and engineer for it
Create repeatable, scalable frameworks for onboarding new client data — not one-off fixes, but systematic approaches that work across our client base
Own data quality and validation — ensuring model inputs are accurate, timely, and trustworthy, because garbage in means garbage out
Translate between worlds — you can speak to clients about their campaign taxonomies and media plans, then turn around and write the dbt models that structure it correctly
Free our scientists — deliver model-ready data so our Chief Data Modelling Officer and marketing scientists can focus on modelling and strategy. 

Deep expertise in data warehousing, data modelling, and pipeline architecture
Strong SQL skills — you can write, optimise, and debug complex queries without breaking a sweat
Experience with marketing data ingestion tools like Supermetrics (or similar: Understanding of ETL/ELT patterns, data quality frameworks, and pipeline orchestration
Ability to design for scale — our solutions serve multiple clients with varying data complexity

You've worked in or with marketing/advertising — agencies, adtech, martech, or brands with sophisticated media operations
You understand the marketing data ecosystem: paid social, programmatic, search, TV , OOH, affiliate, and how they all measure differently
You know why campaign taxonomies matter, what UTM chaos looks like, and how to normalise data across platforms with different naming conventions
You've progressed from hands-on engineering into architecture and technical leadership
You can operate autonomously — we're remote and async-first, so you need to drive your own work
You're comfortable with ambiguity — client data is messy, requirements evolve, and you'll need to figure things out

Communication - strong, clear and frequent communication, at all times


You've onboarded, understood our platform and client base, and taken ownership of data pipelines for several clients. Our scientists are already spending less time on data prep.
Data quality issues are caught before they reach models.
You've transformed how Seeda handles client data. Our senior team is focused on high-value modelling and strategy work, and you've built the data foundation that makes it possible.

Cloud: Google Cloud Platform (GCP)
Data Warehouse: Orchestration: [You'll help us evolve this]