Senior Marketing Cloud Developer

Hike2•Remote, PA

2d•$130,000 - $150,000

About The Position

HIKE2 is looking for a Senior Marketing Cloud Developer to help clients build personalized, data-driven customer engagement on Salesforce Marketing Cloud Engagement and Data 360. You will work directly with clients as a technical advisor and hands-on developer. You will help clients shape requirements and choose an approach, then personally design, build, and troubleshoot the solution. You must stay technically current and balance delivery work with reviewing others' builds and helping less experienced developers grow. Our marketing work spans Marketing Cloud Engagement, Data 360, Salesforce CRM, and the unified customer data that supports segmentation, personalization, and AI. You should be able to make sound technical decisions on your own and help others make them too.

Requirements

5+ years of hands-on Salesforce Marketing Cloud Engagement development, including Journey Builder, Automation Studio, Contact Builder, and Content Builder.
2+ years implementing Data 360 (Data Cloud), including data streams, data mapping, identity resolution, calculated insights, segmentation, and activation, with at least one implementation delivered to production.
Current hands-on proficiency in AMPscript, SSJS, and SQL. You can personally build, test, deploy, and troubleshoot solutions, as well as review others' code and designs.
Experience integrating Marketing Cloud with Salesforce CRM and external systems through Marketing Cloud Connect and REST and SOAP APIs. You can investigate a failed automation, a broken journey, or an incorrect segment count with the team.
A clear understanding of data quality, consent management, deliverability, and privacy regulations such as CAN-SPAM, GDPR, and CCPA.
Experience working directly with clients: leading requirements sessions, explaining tradeoffs to marketing and technology stakeholders, and turning the agreed approach into working builds.
Must reside in the U.S. and hold US Citizenship.

Nice To Haves

Data 360 Ingestion API, web and mobile SDKs, data actions, or zero-copy integration with Snowflake, Databricks, or BigQuery.
Marketing Cloud Personalization, Account Engagement, Mobile Studio, or Einstein features such as Engagement Scoring and Send Time Optimization.
Consulting or professional services experience, including scoping, estimation, and partner collaboration.
Salesforce Flow, Apex, or data preparation for Agentforce and other production AI use cases.
Salesforce certifications such as Marketing Cloud Developer, Marketing Cloud Engagement Consultant, or Data 360 Consultant, backed by demonstrated delivery experience.

Responsibilities

Lead technical design for Marketing Cloud Engagement and Data 360 engagements. Guide data models, journey architecture, integration patterns, and activation approaches, translating marketing goals into decisions that balance deliverability, data quality, maintainability, and cost.
Build and troubleshoot solutions hands-on: Journey Builder journeys, Automation Studio processes, and AMPscript, SSJS, and SQL for dynamic content, CloudPages, and preference centers.
Implement Data 360: configure data streams, map source data to the data model, design identity resolution, and build calculated insights and segments that activate to Marketing Cloud Engagement and other channels.
Integrate Marketing Cloud with Salesforce CRM and external systems through Marketing Cloud Connect, REST and SOAP APIs, and middleware. Work with the team to diagnose sync failures, data mismatches, and send errors.
Establish and reinforce engineering expectations for marketing builds: testing, data validation, rendering and deliverability checks, consent and privacy compliance, documentation, and deployment practices.
Advise client marketing and technology leaders on personalization strategy, data readiness, and priorities for real-time and AI-driven engagement. Clarify data ownership and acceptance criteria, and work with AI colleagues to provide dependable profile data to Agentforce and other AI use cases.
Contribute to discovery, estimates, and user stories. Make dependencies and delivery risks visible, and help clients choose an approach they can support after go-live.
Turn lessons from engagements into maintained patterns, code snippets, and reusable assets that people can apply on their next project.