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)