Data Engineering Lead

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. 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 Data Warehouse : BigQuery Transformation : dbt Infrastructure : Terraform Ingestion : Supermetrics + custom connectors Orchestration : (You'll help us evolve this) Hiring Process Application review Initial conversation #J-18808-Ljbffr