Data Platform Engineer

The Data Platform Engineer is responsible for building, implementing, and maintaining technical solutions that align with best-practice standards and client needs. Working closely with Data Architects, they provide technical execution and development support within Salesforce.com and other SaaS applications to support business and product strategies, with a heavy emphasis on maximizing the Salesforce Data 360 (D360) ecosystem. 

Role Responsibilities:

• Data Architecture & Infrastructure Implementation
• Enterprise D360 Harmonization: Implement and maintain data structures that align with business needs, leveraging Salesforce Data 360 (D360) capabilities for unified profile management, data democratization, and real-time activation. 
• Modern Cloud Integration: Build and deploy data solutions that bridge enterprise cloud data platforms (Data Lakes/Warehouses) with the Salesforce ecosystem to address specific business needs, such as Business Intelligence (BI), ETL/ELT, and AI/ML initiatives.  
• Legacy Migration: Execute the migration, ingestion, and mapping of customer data from legacy systems and siloed databases into the Salesforce D360 platform.  
• Governance & Trust: Configure and maintain data accessibility, data privacy, granular security controls, and compliance with relevant regulations (e.g., GDPR, CCPA) and industry standards within the customer data ecosystem.
• Data Modeling & Pipeline Engineering:
• Ingestion & Streaming Pipelines: Develop, deploy, and maintain real-time streaming and batch data pipelines for ingesting, transforming, and loading high-volume enterprise data into Salesforce D360 from various cloud sources and APIs.  
• D360 Identity Resolution: Build and support scalable data models and metadata architectures within Salesforce D360, implementing identity resolution rules, reconciliation rules, and unified data graphs as designed.  
• Performance Optimization: Monitor, troubleshoot, and optimize query performance, calculated insights, identity resolution runs, and data transformation processes within the Salesforce D360 and underlying lakehouse environments. 
• Technical Execution and Collaboration:
• Cross-Functional Collaboration: Collaborate actively with key stakeholders—including business users, data engineers, data scientists, and CRM IT teams—to understand technical specifications and deliver unified data solutions.  
• Ecosystem Implementation: Help evaluate, test, and integrate appropriate tools, connectors, and zero-copy/Zero-Data-Movement technologies for seamless data integration, transformation, and activation within the Salesforce D360 ecosystem.  
• Technical Guidance: Provide development-level support, code reviews, and best-practice engineering guidance to data engineering and CRM development teams.

 Experience/Skills Required:

• Experience: 3+ years of experience in data engineering, technical consulting, or database development for cloud data platforms with multiple enterprise workstreams.
• Salesforce D360 (Data Cloud) Capabilities: Strong working knowledge of the data cloud architecture, including data models (DMOS, DSOs), identity resolution, data spaces, calculated insights, and activation targets.
• Modern Data Methods: Familiarity and alignment with modern data architecture methods, including lakehouse architecture, zero-copy data sharing, and real-time data activation.
• Broad Data Ecosystem Experience: Hands-on experience with enterprise database technology, cloud data warehouses (e.g., Snowflake, Databricks), ETL/ELT tools, data engineering pipelines, and data science principles.
• Strong Communication: Excellent verbal and presentation abilities, capable of effectively communicating technical engineering concepts and data updates to stakeholders and team members.
• Technical Tooling & Development: Strong proficiency with data-centric programming languages (such as SQL and Python) as well as Salesforce application development components (Apex, Flow, LWCs, MuleSoft).
• Engineering Patterns: Solid understanding of enterprise architecture patterns, API management, and real-time data streaming technologies (e.g., Kafka, Amazon Kinesis).
• Structured Data Modeling: Practical experience with data modeling methodologies (such as Kimball dimensional modeling, Star/Snowflake schemas, or Medallion Bronze/Silver/Gold structures) and mapping them into a canonical Customer 360 model.
• US Authorization: Must have full-time permanent US work authorization.