Our client in Indianapolis, IN, is looking for a contract
Data Platform Engineer to join their team!
This is a hybrid/full-time/12-month contract position
Hourly range based on experience: $80-$85/HR
Our client is looking for a Data Platform Engineer to help us support their continued growth. 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.
A Data Platform Engineer participates in discovery sessions to understand client processes and challenges, translating documented solution designs into functional technical requirements and production-ready implementations. They provide collaborative execution and clear communication to the project and client team members to build a foundation for a trusted technical partnership.
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.
Additional Preferred Experience/Skills:
- Bachelor's Degree preferred, or equivalent experience.
- Salesforce Certified Data Cloud Consultant or Accredited Professional designations.
- Hands-on experience with core Salesforce CRM technology like Sales Cloud, Service Cloud, or Industry Clouds (e.g., Financial Services Cloud, Health Cloud).
- Hands-on experience deploying AI/ML solutions like Salesforce Einstein, AWS Sagemaker, or Google Vertex AI, alongside a solid understanding of Generative AI patterns (e.g., Retrieval-Augmented Generation / RAG).
- Experience designing data architectures on cloud platforms like Amazon Web Services, Microsoft Azure, or Google Cloud Platform.
- Hands-on expertise with analytics and visualization tools like Tableau, CRM Analytics, Power BI, or Looker.
- Open to travel based on client and business demands.
We are an equal-opportunity employer. We do not discriminate in hiring or employment against any individual based on race, color, gender, national origin, ancestry, religion, physical or mental disability, age, veteran status, sexual orientation, gender identity or expression, marital status, pregnancy, citizenship, or any other factor protected by anti-discrimination laws.
