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Babarogic © 2023. Designed by Goran Babarogic

Babarogic © 2023. Designed by Goran Babarogic

GTM Engineering

GTM Engineering

We engineer seamless systems that connect your sales, marketing, and data tools to power efficient, scalable go-to-market operations.

Industry

Fintech

Headquarters

Madrid

Founded

2014

Company size

100+

Links

Overview

GTM Engineering builds the technical foundation that powers go-to-market operations. We connect data, systems, and teams through seamless integrations, creating a unified infrastructure that drives efficiency and scalability.

Role

Our role is to design, build, and maintain the systems that align marketing, sales, and operations. We focus on automation, data accuracy, and scalability—ensuring your GTM stack performs reliably and grows with your business.

Challenge

Organizations struggled with technical bottlenecks that slowed execution and limited visibility.

  • Disconnected CRMs, marketing tools, and analytics platforms.

  • Manual data transfers causing errors and reporting delays.

  • Lack of scalable architecture to support expansion and experimentation.

Solution

We engineered a cohesive GTM system architecture tailored to the organization’s growth stage.

  • Unified Data Pipelines: Automated data flow between Salesforce, HubSpot, and BI tools.

  • API Integrations: Connected sales, marketing, and product data to create a single source of truth.

  • Automation Framework: Built logic-based workflows for lead routing, account assignment, and enrichment.

  • Monitoring and Maintenance Layer: Established real-time error tracking and system health dashboards.

Results

  • 25% Faster Campaign Launches: Streamlined integrations accelerated GTM execution.

  • Improved Data Reliability: Unified data eliminated duplication and inconsistencies.

  • Cross-Functional Visibility: Shared dashboards aligned sales, marketing, and operations.

  • Operational Scalability: Infrastructure built to support new markets, regions, and product lines.

Key Considerations

  • Build flexible data models that evolve with business needs.

  • Maintain automation transparency with clear documentation.

  • Standardize naming and tracking conventions across systems.

  • Invest in ongoing monitoring to ensure performance and reliability.

25%

25%

Faster Campaign Launch Times

35%

Increase In Data Reliability

20%

20%

Reduction In Tech Maintenance Hours

Process

01
Discovery & Data Review

We analyzed sales workflows, CRM data, and pipeline bottlenecks to identify where AI could drive efficiency.

02
Model Design & Training

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

03
Integration & Automation

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

04
Testing & Calibration

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

05
Deployment & Enablement

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

01
Discovery & Data Review

We analyzed sales workflows, CRM data, and pipeline bottlenecks to identify where AI could drive efficiency.

02
Model Design & Training

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

03
Integration & Automation

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

04
Testing & Calibration

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

05
Deployment & Enablement

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

01
Discovery & Data Review

We analyzed sales workflows, CRM data, and pipeline bottlenecks to identify where AI could drive efficiency.

02
Model Design & Training

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

03
Integration & Automation

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

04
Testing & Calibration

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

05
Deployment & Enablement

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

Tools