Marketing Data Engineering Lead
Role summary
Tradeify is seeking a Marketing Data Engineering Lead to build and own their marketing data foundation end-to-end. This role involves designing and implementing ELT/ETL pipelines, identity stitching, and data models to create a unified analytics system across multiple brands. You will work with core platforms like BigQuery, GA4, Mixpanel, Google Ads, Meta Ads, and e-commerce systems to connect marketing spend, user behavior, and transactions into a single source of truth. The position requires strong SQL, data modeling skills, and hands-on experience with marketing analytics platforms and e-commerce data, with a focus on building reliable, automated reporting and measurement plumbing.
The Opportunity
Tradeify is a high-growth retail prop trading firm scaling across multiple sister brands. We are moving from ad-hoc reporting to a warehouse-first, automated analytics system that works across 4-5 companies.
We are hiring a technical builder to own the marketing data foundation end-to-end. This is not a reporting-only role. You will design and implement pipelines, identity stitching, data models, and automated monitoring that connect marketing spend + onsite behavior + platform usage + checkout/transactions into a single source of truth in SQL (or Graph) databases.
You will operate across 4-5 core platforms (example: BigQuery, GA4, Mixpanel, Google Ads/Meta, WooCommerce/WordPress, Intercom, and platform event sources) and build low-touch reporting that stays correct as the business scales.
If you exist at the intersection of
Data Engineering
(SQL/Graph, ETL, Pipelines, Warehousing) and
Growth Marketing
(Pixels, GTM, analytics platforms), this is your chance to build a best-in-class analytics engine from the ground up.
Key Responsibilities
Build and own the multi-brand warehouse-data layer
- Design and implement ELT/ETL pipelines into SQL and/or Graph from 4-5 core sources across 4-5 brands, typically:
- GA4 export (web behavior, acquisition attribution)
- Mixpanel (product and lifecycle analytics)
- Google Ads + Meta Ads spend and conversion data
- E-commerce/transactions (WooCommerce/WordPress and other checkouts as applicable)
- Platform lifecycle events (webhooks/APIs from trading platforms and internal systems)
- Intercom (support/lifecycle signals) as applicable
- Identity stitching (client\_id/user\_id + internal platform ids)
- Sessions and attribution (UTMs, gclid, referrers, first/last touch)
- Transactions (orders, revenue, refunds/chargebacks)
- Product usage and lifecycle milestones (activation, breaches, payouts, upgrades, retention)
Own measurement plumbing where it touches the warehouse (cross-brand)
- Define and enforce event taxonomy, UTMs, and conversion definitions consistently across brands
- Guide GTM/GA4 implementation for reliability (including SPA behaviors where applicable)
- Implement Mixpanel tracking standards:
- Event naming conventions and property standards
- Identity merge rules and distinct\_id strategy
- Revenue event standards (purchase, refunds, chargebacks where applicable)
- Implement server-side ingestion where needed:
- Webhooks -> warehouse, and optional forwarding to GA4/Mixpanel where appropriate
- Deduplication and idempotency (webhooks are at-least-once)
- Implement and maintain Google Ads and Meta conversions (pixel/CAPI where applicable), deduplication, and diagnostics
Decision-ready reporting built on the warehouse
- Build a small number of durable dashboards (Tableau preferred, or Power BI/Looker Studio)
- Deliver both:
- Brand-level performance views (CAC, funnel health, revenue)
- Cross-brand executive rollups (portfolio channel mix, payback, cohort outcomes)
- Enable self-serve slices (brand, channel, campaign, cohort, lifecycle stage) without creating 50 dashboards
Required Experience and Skills
- 4+ years in marketing data, marketing analytics engineering, or growth analytics where you owned pipelines and data models (not just pulling reports)
- Strong SQL and comfort building in BigQuery (or equivalent warehouse)
- Proven experience designing ELT/ETL pipelines across multiple SaaS platforms and APIs/webhooks
- Strong practical data modeling skills (staging -> intermediate -> marts), including join keys, deduplication, and incremental loads
- Hands-on experience with:
- GA4 and GTM (including BigQuery export concepts)
- Mixpanel implementation and governance (event taxonomy, identity merge, revenue events)
- Google Ads and Meta Ads data (spend, conversions, attribution realities)
- E-commerce/transaction data (WooCommerce/WordPress or similar)
- Dashboarding experience (Tableau preferred, or Power BI/Looker Studio) built on warehouse tables, not direct connectors
- Ability to translate ambiguous questions into data contracts, models, and automated reporting
Nice to Have
- dbt or Dataform (or similar) for modeling discipline
- Python for automation, QA, and API ingestion
- Experience with low-maintenance ingestion tools (Airbyte/Fivetran/Stitch) and knowing when not to use them
- Server-side tracking experience (server-side GTM, CAPI patterns, offline conversion uploads)
- Intercom extraction and analysis (or similar messaging/support platforms)
- Experience integrating affiliate platforms, CRM/CDP tools, or payment processors (Stripe-like)
- Experience in regulated or policy-sensitive categories (fintech, trading, gambling-adjacent ad policy environments)