Data Platform Engineer (Data 360)

Role Responsibilities

• Data Architecture & Infrastructure Implementation
• Enterprise D360 Harmonization
• Modern Cloud Integration
• Legacy Migration
• Governance & Trust
• Data Modeling & Pipeline Engineering
• Ingestion & Streaming Pipelines
• D360 Identity Resolution
• Performance Optimization
• Cross-Functional Collaboration
• Ecosystem Implementation
• Technical Guidance

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.