Data Platform Engineer; Data
Listed on 2026-07-18
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IT/Tech
Data Engineering, Cloud Computing: Infrastructure & Operations, Data Warehousing
Location: Indianapolis
We are looking for a Data Platform Engineer to help us support our continued growth. The Data Platform Engineer is responsible for building, implementing, and maintaining technical solutions, ensuring they align with best practice solutions based on client needs. Working closely with Data Architects, they provide technical execution and development support within and other SaaS applications to support business and product strategies, with a heavy emphasis on maximizing the Salesforce Data 360 (D360) ecosystem.
We will participate discovery sessions to understand client processes and challenges, translating documented solution designs into functional technical requirements and production-ready implementations. We provide collaborative execution and clear communication to project and client team members in order to build a foundation as a trusted technical partner.
This position will require a weekly onsite presence at our client's location in Indianapolis, IN.
Role Responsibilities:- 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. - 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.
- Cross-Functional Collaboration:
Skills Required:
- Experience:
3+ years of experience in data engineering, technical consulting, or database development for cloud data platforms with multiple enterprise work streams. - 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…
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