Marketing Technology Strategist

About the role

This organization operates in the marketing technology consulting space, focusing on building data systems that support clients'' growth initiatives. It addresses the need for reliable, scalable data architecture that connects raw data to actionable business outcomes like acquisition, lifecycle marketing, attribution, and audience activation. The company collaborates directly with clients to design and implement cloud warehouses, pipelines, customer data platforms, and platform integrations, emphasizing data quality and observability to enhance analytics and AI results.

The Marketing Technology Strategist role is designed for an experienced data engineer transitioning into a strategic, client-facing position. The role involves designing and building data architectures and pipelines that drive marketing outcomes, while also advising clients on technical strategy and architecture decisions. This position contributes significantly by ensuring data systems enable trusted analytics and activation, directly impacting clients'' marketing effectiveness and growth.

Responsibilities

• Design data models, schemas, and identity strategies linking raw data to business and marketing outcomes.
• Build and maintain ETL and ELT pipelines and transformations in cloud data warehouses.
• Integrate warehouses, CDPs, CRM, marketing tools, messaging platforms, analytics, and media data sources.
• Configure audience activation and lifecycle signal flows using Reverse ETL techniques.
• Develop attribution frameworks, UTM structures, and campaign tracking for performance measurement.
• Prepare data for AI-driven use cases including agents, automations, and analytics assistants.
• Test, monitor, document, and improve data systems for reliability and scalability.
• Collaborate directly with client stakeholders to present architecture and align technical decisions.
• Contribute to practice development by creating reusable data engineering patterns and frameworks.

Requirements

• Minimum 6 years of hands-on data engineering experience in production, with at least 2 years in an agency or consulting role.
• Proficiency in Python and SQL with experience building production-grade pipelines and data models.
• Experience with cloud data warehouses such as Snowflake, BigQuery, or Redshift, and transformation tools like dbt.
• Knowledge of CDPs (e.g., Segment, mParticle), event collection systems, and orchestration tools (e.g., Airflow, Dagster).
• Experience integrating CRM and marketing automation platforms (e.g., HubSpot, Salesforce, Iterable, Braze).
• Hands-on experience with Reverse ETL tools (e.g., Census, Hightouch), web analytics, and marketing measurement.
• Familiarity with AI tools and daily use of coding assistants (e.g., Claude Code, Copilot, Codex).
• Strong client communication skills, comfortable presenting technical strategies and decisions.