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Ensure your Snowflake doesn't sit idle with expert Snowflake integration and implementation services

Most teams invest heavily in Snowflake, but it ends up powering dashboards instead of decisions. With Mavlers' Snowflake integration and implementation services, your data foundation is built for marketing activation so every campaign, audience, and journey runs on real-time, trustworthy data, not static reports.

From schema design to Snowflake Cortex AI integration to Reverse ETL activation via Hightouch, we own the entire pipeline end-to-end.

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Why Snowflake CDP alone is not driving marketing outcomes

Snowflake is treated as a BI tool, not a marketing engine

Schemas and pipelines for identity resolution, segmentation, and scoring aren't built, so clean data never powers campaigns.

Warehouse data never reaches your MAPs

LTV, churn, and RFM models exist in Snowflake, but never sync to tools like SFMC, Braze or Klaviyo for activation. The Reverse ETL activation layer is missing.

No dbt marketing model layer

Raw event tables exist, but no structured dbt model layer for marketing, leading to one-off queries, inconsistent logic, and slow audience creation cycles.

Credit costs scale faster than data value

Without governance, optimization, or monitoring, Snowflake credit usage grows unpredictably while marketing and finance struggle to connect rising costs to tangible business outcomes.

Data sharing with partners is manual

Audience sharing still relies on CSV exports and manual uploads, while Snowflake's native secure data sharing and clean room capabilities remain completely underutilized.

Snowflake Cortex AI is unlicensed or unused

Snowflake Cortex AI remains inactive, leaving valuable signals like sentiment, support insights, and product feedback unused for personalization and smarter decision-making.

If your Snowflake powers dashboards but not decisions, you're missing the real value.

Activate your data and make every campaign driven by real-time customer behavior with Mavlers' Snowflake implementation services.

From data to marketing revenue- case studies that show how it's done

Media Group: Unified Subscriber Data Platform on Snowflake

Industry

Media & publishing · Multi-product subscription · B2C · 4M+ subscribers

Challenge

Data fragmented across subscription, editorial, and ads systems. No cross-product visibility, slow churn detection (3 weeks), and Snowflake used only for BI, no activation layer.

Solution

Mavlers built a Snowflake Customer 360 with dbt models (RFM, engagement tiers, upgrade propensity). Snowflake Cortex AI used for sentiment scoring. Activation via Hightouch into Salesforce Marketing Cloud. Cost governance implemented.

Outcome

  • +31% upgrade performance
  • Churn detection: 3 weeks → 48 hours
  • 100% campaigns using engagement data
  • 28% reduction in Snowflake costs
Case study 01: upgrade performance, churn detection speed, campaigns using engagement data, Snowflake cost reduction.

Stack

Snowflake · Fivetran · dbt · Snowflake Cortex AI · Hightouch · Salesforce Marketing Cloud · Streamlit

E-Commerce Brand: dbt Attribution and LTV Modelling

Industry

E-commerce · D2C · Multi-channel · 1.2M+ customers

Challenge

Last-click attribution only. LTV in spreadsheets. No reliable channel optimization. Snowflake lacked a modeling layer; no activation via Hightouch.

Solution

Mavlers built a dbt marketing mart (Markov attribution, cohort LTV, RFM). Snowflake Cortex AI used for sentiment scoring. High-LTV cohorts and suppression audiences activated to paid channels via Hightouch.

Outcome

  • 23% conversion credit reattributed beyond last-click.
  • +18% ROAS from better channel allocation.
  • Weekly automated LTV modelling.
  • +26% improvement in lookalike CPM efficiency.
Case study 02: conversion credit beyond last-click, ROAS lift, LTV modelling, lookalike CPM efficiency.

Stack

Snowflake · dbt · Snowflake Cortex AI · Hightouch · Braze · Meta · Google Ads · TikTok

Strict NDAs limit client disclosure; our current engagements include multiple brands and agencies.

Disney
DMA
Ogilvy
National Geographic
Penguin Random House
Incubeta
Oracle
Airtasker

In the words of our clientsheart

Cloth & Co.

Having one partner who can effectively support multiple areas of our digital presence is both efficient and valuable. The partnership works best when you can collaborate...

Daisy Burgess

Operations Manager

The Maze Group

They bring subject matter expertise to every engagement and clearly identify blockers, dependencies, and requirements. They prioritize proactive communication and problem...

Mathew Feuer

Director of Retention

PetLab Co

Mavlers was clear with communication and provided top-level support. We were on a really tight deadline to get a high priority project across the line and without them we...

Scott Bartlett

Director of Retention

From Snowflake setup to campaign-ready data and board-ready attribution - our 3-phase model

Phase01

Architect

Weeks 1–4

What happens here

  • Account configuration: multi-cloud (AWS / Azure / GCP), virtual warehouse sizing, auto-suspend policies, role-based access control.
  • Database and schema architecture: marketing data model design covering customer, event, product, and order entities.
  • Data ingestion setup: Fivetran, Airbyte, or custom connectors for all marketing sources.
  • Snowpipe for continuous event loading from Segment, RudderStack, or Kafka.
  • Dynamic Data Masking and Row Access Policies for PII governance.
What you get: Snowflake environment live + ingestion running
Phase02

Model

Weeks 3–8

What happens here

  • Customer 360 table design: unified identity spine with behavioral, transactional, and firmographic attributes.
  • dbt project setup: staging, intermediate, and mart layers aligned to marketing use cases.
  • Attribution models in SQL: last-touch, linear, time-decay, and data-driven Markov chain, RFM scoring, LTV modeling, cohort retention, and churn risk.
  • Snowflake Cortex AI: sentiment analysis, NLP classification, and LLM-powered SQL for marketing analysts.
What you get: Marketing data models + scores live in Snowflake
Phase03

Activate

Weeks 7–12

What happens here

  • Hightouch Reverse ETL: audience syncs and attribute pushes to Braze, SFMC, Klaviyo, HubSpot, Meta, Google Ads, and TikTok.
  • dbt Semantic Layer exposure to Hightouch and BI tools via a single defined metric layer.
  • Snowflake Secure Data Sharing: audience sharing with media agency partners without data movement.
  • Cost governance: resource monitors, query profiling, credit optimization dashboards via Streamlit.
  • Board-ready marketing attribution dashboard.
What you get: Audiences in MAP + attribution live + cost governance active

Mavlers- a trusted partner who owns your data pipeline + campaign execution layer

Proven at scale

500+ marketing data pipelines delivered across industries, giving us the pattern recognition to design, build, and activate Snowflake setups that actually drive outcomes.

Marketing-first schema design

We design schemas for activation, not just reporting, so data flows seamlessly into Reverse ETL via Hightouch, powering audiences, journeys, and real-time triggers.

Built-in cost efficiency

Cost governance isn’t optional. Our frameworks typically reduce Snowflake spend by 25–40% within 60 days through optimisation, monitoring, and smarter warehouse management

Cost governance as standard

From auto-suspend policies to clustering optimisation, we embed governance into every implementation, ensuring your Snowflake scales efficiently alongside business value.

Early advantage in Snowflake Cortex AI

We implement Snowflake Cortex AI to unlock LLM-powered use cases, like sentiment analysis, classification, and personalization signals, before most teams even activate it.

End-to-end ownership across the stack

From Snowflake architecture to dbt models to Snowflake integration and activation in Braze; we own the full pipeline, eliminating gaps between data and marketing execution.

Find out what your Snowflake should be powering, and your fastest path to campaign revenue, with a no-obligation consultation.

Book a 15-min call

Engage your way, wherever you are in your Snowflake activation journey

Foundation sprint

Best for

New Snowflake setup + first marketing activation

What's included

Architecture + ingestion + dbt staging + first Hightouch audience live in MAP

Typical duration

8–12 weeks Fixed scope

Marketing data retainer

Best for

Ongoing dbt modeling + activation expansion

What's included

Dedicated Snowflake engineer + dbt analyst · Monthly model additions and OKRs

Typical duration

12+ months Monthly retainer

Cost optimization sprint

Best for

Reducing Snowflake credit consumption

What's included

Warehouse audit + query optimization + governance dashboards

Typical duration

4–6 weeks Fixed scope

Cortex AI sprint

Best for

Activating Snowflake Cortex AI for marketing

What's included

Sentiment models + Copilot configuration + Cortex attributes synced to MAP via Hightouch

Typical duration

3–5 weeks Fixed scope add-on

Embedded team

Best for

Scaling in-house Snowflake data team

What's included

Mavlers engineers in your Jira/Slack · T&M billing · Ramp up/down quarterly

Typical duration

Flexible Ongoing

Snowflake or not? Here's your decision guide.

Marketing ecosystem fit

Snowflake

Best - widest Reverse ETL connector support, Hightouch native

Google BigQuery

Strong - Google Ads + GA4 native integration

Databricks

Strong - ML-heavy and streaming stacks

Amazon Redshift

Moderate - AWS ecosystem primarily

Concurrency and scaling

Snowflake

Multi-cluster warehouse, near-unlimited concurrency

Google BigQuery

Serverless - auto-scales, pay-per-query

Databricks

Cluster-based - manual tuning required

Amazon Redshift

Concurrency scaling add-on, limited

AI and ML capabilities

Snowflake

Snowflake Cortex AI (GA 2024/25), Snowpark ML

Google BigQuery

Vertex AI integration - strong

Databricks

Native MLflow - best ML platform overall

Amazon Redshift

SageMaker integration

Data sharing and clean rooms

Snowflake

Best-in-class: Secure Data Sharing + Snowflake Clean Rooms

Google BigQuery

Analytics Hub (improving)

Databricks

Delta Sharing (open standard)

Amazon Redshift

Data Exchange (limited)

Cost model

Snowflake

Storage + compute credits - predictable

Google BigQuery

On-demand or flat-rate slots - best for variable workloads

Databricks

DBU compute-based - can spike

Amazon Redshift

RA3 storage + compute

Ops overhead

Snowflake

Minimal - fully managed, no DBA required

Google BigQuery

Serverless options - very low

Databricks

Requires data engineering expertise

Amazon Redshift

More DBA overhead than Snowflake or BigQuery

Mavlers recommendation

Snowflake

SQL-first analytics and multi-MAP activation. Best default for marketing data foundation.

Google BigQuery

GCP-native orgs, Google Ads-first activation, or Looker as primary BI tool.

Databricks

ML workloads, streaming data pipelines, Databricks-invested data science teams.

Amazon Redshift

AWS-native orgs heavily invested in Redshift infrastructure.

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Frequently asked questions

We already have Snowflake. What does Mavlers add beyond what we've built?

Most Snowflake CDP environments we audit have solid ingestion pipelines but weak or absent marketing model layers. The most common gaps: no dbt Mart layer for marketing use cases, no Hightouch Reverse ETL configured for MAP activation, Snowflake Cortex AI unlicensed or unused, and no cost governance policies. We audit what exists and fix specifically what's missing, without rebuilding what already works.

Do we need Databricks if we already use Snowflake for analytics?

For most mid-market marketing stacks, no. Snowflake with Snowpark and Cortex AI handles the majority of ML use cases relevant to marketing, churn scoring, LTV modeling, propensity scoring, and sentiment analysis without the additional cost and operational complexity of a Databricks environment. Databricks makes sense when you have streaming data pipelines at scale, complex deep learning workloads, or a data science team already deeply invested in the Spark and MLflow ecosystem. We help you make this decision based on your actual workloads, not vendor positioning.

How do you connect Snowflake data to our MAP (Braze, SFMC, Klaviyo)?

We configure Hightouch as the Reverse ETL activation layer. Hightouch connects to your Snowflake tables or dbt Semantic Layer via SQL queries we design audience queries, trait definitions, suppression logic and syncs computed outputs to your MAP on a defined schedule or near-real-time. We handle schema mapping, sync scheduling, field-level validation, and failure alerting. First audience syncing live typically within 2–3 weeks of Hightouch configuration.

What is Snowflake Cortex AI, and what does it do for marketing teams?

Snowflake Cortex AI reached general availability in 2024 and is now included in Snowflake Enterprise. Marketing use cases we implement: sentiment analysis on customer reviews, NPS responses, and support tickets (scored and stored as Snowflake attributes, synced to MAP via Hightouch for personalisation); Snowflake Copilot (natural language to SQL for marketing analysts who don't write SQL); CORTEX COMPLETE for LLM-powered content classification and translation inside Snowflake without data leaving your security perimeter; and Snowpark ML for churn and LTV model training in Python inside Snowflake.

What is the dbt model layer, and why does every Snowflake implementation need one?

dbt (data build tool) is the transformation layer that sits between your raw Snowflake tables and the marketing-ready outputs that campaigns can act on. Without dbt, analysts write one-off SQL queries that are untested, undocumented, and fragile. With a dbt mart layer, you have tested, documented, version-controlled marketing models - RFM scores, LTV tiers, churn risk scores, attribution models - that refresh automatically and feed Hightouch syncs, BI tools, and Cortex AI models from a single source of truth. We consider a dbt Mart layer mandatory for any Snowflake environment used for marketing activation.

How do you manage Snowflake costs as data volumes grow?

We implement a four-layer cost governance framework: (1) warehouse auto-suspend and auto-resume policies to eliminate idle credit consumption; (2) query profiling with clustering key recommendations and materialised view strategy to reduce scan volume; (3) resource monitors with credit limit alerts per warehouse, per department, and per month; (4) a Streamlit-based cost dashboard showing credit consumption by warehouse, user, and query type. Clients who implement this framework consistently see 25–40% reduction in Snowflake spend within 60 days.

What data sources can you connect to Snowflake?

We configure ingestion pipelines for all major marketing data sources: CRM (Salesforce, HubSpot), e-commerce (Shopify, Stripe, Recharge), marketing cloud (Braze, SFMC, Klaviyo, HubSpot), product analytics (Amplitude, Mixpanel, Heap), event streaming (Segment, RudderStack, Kafka), advertising (Meta Ads, Google Ads, TikTok), and web analytics (GA4). Connector technology: Fivetran (primary), Airbyte (open-source option), or custom ELT pipelines for connector gaps.

Can Snowflake replace a packaged CDP like Segment?

For warehouse-mature organizations with a data engineering team, yes, through a composable CDP architecture. Snowflake serves as the customer identity layer, dbt handles modeling and scoring, and Hightouch activates audiences and attributes to MAPs and ad platforms. This typically delivers equivalent activation capability at 40–65% lower infrastructure cost with full data ownership. It is not the right choice for every organization: it requires engineering investment upfront that a packaged CDP packages together. We scope both options during discovery and provide a total cost of ownership comparison.

How do you handle data privacy and compliance in Snowflake?

We implement compliance controls as a standard part of every engagement: Dynamic Data Masking for PII at the column level, Row Access Policies for data residency and team-level access control, audit logging for all data access events, and consent signal ingestion from OneTrust, Cookiebot, or custom CMP integrations. For regulated industries (financial services, healthcare, public sector), we scope HIPAA-eligible configuration, FCA data residency requirements, and GDPR Article 25 privacy-by-design architecture.

Insights to help you win with lifecycle marketing