Data Engineering Lead

Seeda

Australia

Remote

AUD 100,000 - 130,000

Full time

14 days+

Application generator

Stand out for this role — generate a tailored resume and cover letter in about a minute.

Get past ATS filters

Job summary

A forward-thinking startup is seeking a Marketing Data Engineering Lead to transform fragmented marketing data into actionable insights. You will design scalable data pipelines and ensure high data quality, unlocking potential for innovative marketing strategies. The ideal candidate has deep expertise in data architecture, strong SQL skills, and marketing domain experience. This full-time, global remote role offers the chance to shape data foundations for delivering strategic marketing insights across various clients.

Qualifications

Experience working in marketing or advertising.
Understanding of the marketing data ecosystem.
Ability to communicate with technical and non-technical stakeholders.

Responsibilities

Design and build robust data pipelines across multiple clients.
Establish data models and schemas for Marketing Mix Modelling.
Own data quality and validation for accurate model inputs.
Create scalable frameworks for onboarding client data.
Translate client needs into actionable technical specifications.

Skills

Deep expertise in data warehousing

Strong SQL skills

Production experience with GCP

Experience with marketing data ingestion tools

Understanding of ETL/ELT patterns

Ability to design for scale

Tools

GCP

BigQuery

dbt

Terraform

Supermetrics

Job description

Get AI-powered advice on this job and more exclusive features.

Direct message the job poster from Seeda

Founder/CEO @ Seeda | Democratising Marketing Mix Modelling for the Mid-Market

Global Remote Role: Data Engineering Lead (Marketing)

About Seeda

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.

Seeda is a Marketing Mix Modelling (MMM) SaaS platform and professional services company. We help brands understand what's actually driving their marketing performance — cutting through attribution chaos to deliver clear, actionable insights on marketing effectiveness.

Our platform and team enable clients to make smarter investment decisions across their entire media mix. But here's the reality: before any model can run, someone needs to wrestle each client's fragmented, messy marketing data into shape.

The Problem We're Solving (With You)

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.

There's a capability gap in the middle.

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.

The Role

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.

You will:

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.

What you bring

Technical Foundation

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
Production experience with our stack: GCP, BigQuery, dbt, Terraform
Experience with marketing data ingestion tools like Supermetrics (or similar: Fivetran, Funnel, etc.)
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

Marketing Domain Expertise:

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
Bonus: familiarity with Marketing Mix Modelling, attribution, or marketing measurement concepts

How You Work:

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 communicate clearly with both technical teams and non‑technical stakeholders (including clients)
You're comfortable with ambiguity — client data is messy, requirements evolve, and you'll need to figure things out
Ownership and high care factor - Be excited about building this revolutionary product with us, and proactively take ownership. Tell us what we need to do, and bring your ideas to the table. Ask us questions like: Have you thought about doing this or that….
Communication - strong, clear and frequent communication, at all times

What Success Looks Like

In 3 months: 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.
In 6 months: You've established patterns and frameworks that make new client onboarding faster and more reliable. Data quality issues are caught before they reach models.
In 12 months: You've transformed how Seeda handles client data. The capability gap is closed. Our senior team is focused on high-value modelling and strategy work, and you've built the data foundation that makes it possible.

Our Stack