Track Pillar · Data & Analytics Engineering

We don't just add tags. We engineer your analytics stack.

This is where we stop being “an analytics agency” and become your data engineering partner for digital. From DOM scraping and data-layer design to server-side events, ETL pipelines, warehouses and embedded AI, we treat measurement like a software product—not a checklist.

Well-designed data layers instead of brittle DOM hacks.
Streaming + batch pipelines from GA4, product and backend into BigQuery, Snowflake or your warehouse of choice.
A technical architecture that can actually support AI-driven reporting, LTV modelling and decision automation.

Architect my analytics stack See our architecture approach

Built for teams who know “just add an event in GTM” isn't a strategy: growth, product, data and engineering leaders.

High-Level Architecture · Web Analytics Stack

Capture

DOM listeners · dataLayer pushes · SDK events · backend webhooks.

Transport

GTM / sGTM · Measurement Protocol · streaming into warehouse · queueing & retries.

Model

dbt models · event → session → user modelling · attribution tables · feature stores for AI.

Activate

BI (Looker Studio, Power BI) · reverse ETL into CRM/ads · LLM-based insight layers and alerting.

Event-first

Everything starts from a clean, versioned event schema.

Source of truth

Warehouse-first design, not “what GA4 happens to show.”

AI-ready

Data structured for modelling, not patchwork dashboards.

Why this practice exists

Because "just throw it in GTM" doesn't scale.

The Data & Analytics Engineering practice is where we fix the underlying architecture so Track, Analyze and Optimize stop fighting the plumbing and start working on outcomes.

Tags grew like weeds; nobody owns the garden.

Multiple agencies and devs have added tags over the years. There’s no clear event schema, no ownership and no way to tell what can be safely turned off.

Product, marketing and data teams aren’t looking at the same truth.

GA4, product analytics, backend and finance all disagree. Every strategic conversation starts with, “Which number is right?” instead of, “What should we do?”

Your stack can’t support the AI or modelling you want.

Everyone’s talking about LTV models and AI assistants. Under the hood, your data is still a mix of CSV exports, half-baked events and undocumented joins.

Engineering is tired of band-aid analytics tickets.

Dev teams get hit with random ‘add this event’ or ‘fix this pixel’ requests that don’t tie into any broader architecture. It slows them down and leaves you with more tech debt.

Architecture Approach

Event-first architecture, warehouse as the source of truth.

Everything we do hangs off a clean, documented event schema. From there we architect capture, transport, modelling and activation so you can run advanced analysis and AI without redoing the plumbing every quarter.

Capture

Clean event capture at the edge.

DOM listeners, data-layer design, SDKs and backend emission patterns that avoid duplication and missing context.

DOM scraping only where necessary, wrapped in resilient utilities.
Well-structured dataLayer objects with versioned schemas.
Backend events for critical milestones (e.g. payments, lifecycle).
Standardised event naming across web, app and backend.

Transport

Durable movement from edge → core.

We design the pipes, not just the tags: retry logic, idempotency and observability across the path.

Client → sGTM → destination routing for analytics + ads.
Measurement Protocol for server-side GA4 events.
Webhooks and queues to decouple product/backend from downstream tools.
Logging and tracing around event delivery success/failure.

Model

Warehouse-first modelling.

Events turn into sessions, users, revenue and features—without fragile, one-off SQL living in dashboards.

dbt models for event, session, user and funnel tables.
Attribution-ready tables (touchpoints, paths, conversions).
Data quality checks, freshness SLAs and contracts.
Feature stores powering ML models and AI systems.

Activate

Surfaces for humans and machines.

BI, reverse ETL and AI layers wired into the same core model—so everything pulls from the same truth.

Looker Studio / Power BI semantic layers on top of warehouse.
Reverse ETL into HubSpot, Salesforce, ad platforms and product tools.
LLM-powered insight layers and anomaly detectors.
Playbooks and APIs for downstream teams to safely consume data.

We speak in schemas, not slides

Event models, not mystery events.

This is the difference between “we added a purchase event” and “we engineered a purchase model the whole company can rely on.” We define what good looks like in code, not just documentation.

Versioned event schemas with strict types and required fields.
Mapping from dataLayer → GTM → GA4 / warehouses defined once, reused everywhere.
Clear conventions for identities (user_id, device_id, email), currencies, timestamps and attribution keys.

Example · Purchase event schema (simplified)

{
  "event_name": "purchase",
  "version": 3,
  "required": [
    "user_id", "transaction_id", "value", "currency",
    "items", "source", "timestamp"
  ],
  "properties": {
    "user_id": "string",         // internal user id
    "transaction_id": "string",  // stable across GA4, Ads, backend
    "value": "number",
    "currency": "string",
    "items": [
      {
        "item_id": "string",
        "item_name": "string",
        "quantity": "number",
        "price": "number"
      }
    ],
    "source": "string",          // web, app, offline
    "channel": "string",         // paid_search, paid_social, organic...
    "timestamp": "ISO8601"
  }
}

This is the level we operate at: explicit fields, consistent ids, one schema feeding GA4, Ads and your warehouse.

What we actually build

From instrumentation patterns to full analytics platforms.

We can come in for a targeted rebuild (e.g. purchase infrastructure) or architect the entire analytics platform end to end. Either way, the deliverable is code, pipelines and documentation—not just slides.

Data-layer & event architecture

From brittle event soup to a coherent, versioned schema.

Audit of existing GTM, DOM listeners and dataLayer usage.
Design of canonical event schema for web + app + backend.
Implementation patterns for dataLayer pushes and SDK events.
Governance: ownership, versioning and rollout process.

Server-side & backend events

Critical events owned by your systems of record.

Design of backend event emission patterns (e.g. payment, subscription).
GA4 + Ads + Meta events sent via Measurement Protocol and sGTM.
Idempotency, retries and logging around event delivery.
Alignment of backend truth with analytics & ads conversions.

ETL / ELT pipelines & warehousing

The warehouse becomes the source of truth, not an export bin.

Pipeline design from GA4, product, CRM and billing systems.
Implementation with modern ELT tools or custom jobs (where needed).
Data contracts, quality checks and documentation.
BigQuery, Snowflake, Redshift or your preferred stack.

Modelling & semantic layers

From raw events to decision-grade models.

dbt models for sessions, funnels, cohorts and attribution.
Layered models for marketing, product and finance views.
Re-usable metrics definitions (LTV, CAC, payback, retention).
Semantic layers for Looker Studio, Power BI or other BI tools.

AI-ready analytics foundations

Get the plumbing right before you plug in the AI.

Feature definition and stores for ML models.
Event and table design optimised for LLM-based insight agents.
APIs and guardrails for internal AI tools to safely query data.
Anomaly detection and alerting woven into the measurement stack.

Engineering enablement & handoff

We leave behind systems, not mystery dashboards.

Playbooks and runbooks for engineers and data teams.
Code repositories, infra definitions and IaC where appropriate.
Onboarding sessions for internal dev, data and marketing teams.
Optional ongoing partnership for new products and markets.

The kinds of stacks we live in

Modern data stacks with messy real-world constraints.

We're not dogmatic about tools. The point is architecture: event-first, warehouse-centric, and built to survive real marketing, product and finance pressure.

We're comfortable parachuting into half-built stacks and untangling them—no need for a blank slate.

Capture & tagging

GA4GTMsGTMMeta Pixel / CAPIcustom SDKs

Pipelines & orchestration

Cloud Functions / LambdasPub/Sub / SQSAirflowdbt Cloud

Warehousing

BigQuerySnowflakeRedshiftPostgres

Activation

Looker StudioPower BIHubSpotSalesforcereverse ETL tools

AI & modelling

Python / PandasLLM-based agentsfeature storesanomaly detection

How this lands for different teams

Same architecture. Different wins for each stakeholder.

The point of all this engineering isn't prettier diagrams. It's fewer arguments, faster shipping and more confident bets across the org.

Bring engineering, marketing and finance into the same conversation. We'll talk to all three in one working session.

EngineersFor engineering & data teams

This should not feel like more chaos.

We bring requirements with schemas, not random tickets.
We design around your existing infra and coding standards.
We handle a lot of the tagging + analytics logic so you don’t have to.
We document everything in repos, READMEs and runbooks—not hidden decks.

MarketingFor marketing & growth

You finally get numbers you can fight for.

We tie events and models directly to campaigns, funnels and KPIs.
We make it clear what you can and cannot trust today.
We give you a roadmap for improving measurement, not just a one-off fix.
We keep you looped in on trade-offs so you can explain them internally.

LeadershipFor leadership & finance

You stop funding decisions on vibes.

We align analytics with backend and billing so revenue numbers match.
We define agreed metrics (LTV, CAC, payback, retention) in the warehouse.
We surface a small set of decision-grade views instead of dashboard sprawl.
We give you confidence when you ramp or cut channels and bets.

How we work with your dev team

We reduce noise for engineering instead of adding to the queue.

Most engineers have been burned by “measurement work” before: undocumented scripts, fragile GTM hacks, and surprise production changes. We work with dev teams the way they want to be worked with.

What we don't do

Random one-off tickets like “add this pixel” with no schema or context.
Agencies editing production code or GTM directly with zero review.
No staging / testing path—everything hits live traffic first.
Dashboards that don’t reflect how the data is actually produced.

What we do instead

We start with architecture and schemas, then turn that into concrete tickets.
We respect your branching, review and deployment processes.
We prefer staging-first instrumentation with explicit test cases.
We keep analytics logic (naming, mapping, modelling) on our side; your team focuses on clean emission points.

How collaboration looks in practice

Architecture deep-dive with engineering + data leads.
Written design docs with diagrams, schemas and trade-offs.
Git-based collaboration (PRs, reviews, comments) where needed.
Regular check-ins during implementation; async outside of that.

The goal: your engineers see us as an extension of their team, not another source of chaos.

Selected engineering engagements

When we've rebuilt measurement as infrastructure, not overhead.

We anonymize names when needed, but the patterns repeat: fractured stacks, weak schemas, and teams who know they're flying blind. Data & Analytics Engineering is how we fix that for good—not just for the next campaign.

E-commerce · Global DTC brand

Problem: GA4, Shopify and billing all disagreed; no clean way to run LTV or cohort analysis. Tags had accreted across multiple agencies.

What we did: Redesigned dataLayer + event schema, moved purchase to backend-owned events, built BigQuery + dbt models, and layered Looker Studio + reverse ETL into CRM and ads.

Impact: Single source of truth for revenue and cohorts, automated reporting for leadership, and a data foundation ready for LTV and bidding models.

SaaS · PLG product

Problem: Product events, marketing events and billing were all tracked in different tools with no shared ids or schema.

What we did: Created unified event model across web, app and backend; piped into warehouse, modelled funnel + activation tables and wired into BI + experimentation tooling.

Impact: Clear PLG funnel from first touch to expansion, ability to target and measure specific activation steps with confidence.

Healthcare · Multi-system environment

Problem: Leads and appointments flowed through multiple systems (site, booking engine, EMR, CRM). No unified view and very little room for sloppy compliance.

What we did: Designed privacy-aware event and data architecture, stitched identities across systems, built warehouse models and surfaced compliant views for marketing + operations.

Impact: Reliable, auditable view of demand, throughput and marketing contribution—without compromising on privacy constraints.

Track → Analyze → Optimize

This practice is the bridge between tags and serious analytics.

Track ensures events are captured. Data & Analytics Engineering ensures those events are coherent, durable and warehouse-first. Analyze and Optimize rely on this practice to do anything beyond surface-level dashboards.

Track

Clean, consent-aware capture of key events across web, app and backend.

Data & Engineering

Architecture, pipelines, models and semantics that turn raw events into a decision platform.

Analyze & Optimize

Deep analysis, experimentation and AI-driven optimisation built on a durable foundation.

Explore Analyze Explore Optimize