As a Salesforce Data Cloud Consultant, your responsibilities will include:
- Developing, building, and implementing Salesforce Data Cloud instances in conjunction with Salesforce Marketing Cloud (Engage/Personalize) and/or Salesforce Sales/Service.
- Driving end-to-end project execution, encompassing analysis, documentation, solution development, testing, and performance, to meet client expectations.
- Clearly translating and communicating technical requirements and solutions to the business using story-based narratives, and presenting strategies and recommendations to executives.
- Leveraging knowledge of new and upcoming features in the Salesforce ecosystem to recommend more efficient solutions.
- Assessing client requirements, proposing effective solutions, and communicating the capabilities and limitations of Salesforce Data Cloud, Marketing Cloud Engagement, Personalization, Intelligence, Sales, Service and other relevant SF ecosystem platforms.
- Identifying client process pain points and gaps in best practices and providing effective solutions.
Requirements:
- Experience with Marketing Cloud Suite (Personalization, Intelligence, Engagement, etc. and/or with Sales/Service Suite.
- Experience with Segmentation strategy.
- Customer Data Modeling best practices.
- Salesforce cross-cloud integrations.
- Proficiency in moderately complex SQL queries.
- Experience in integrating with cloud-based data warehouses/data lakes (e.g., Big Query, Snowflake, Databricks).
- Relevant Salesforce certifications (e.g., Data Cloud Consultant Certification, Salesforce Marketing Cloud Consultant), or other clouds) are a plus.
- French & English fluency.
- Bachelor’s or master’s degree in computer science, Information Technology, or a related field.
- 2+ years in marketing automation, data model and architecture in the Salesforce ecosystem.
- 2+ years in consulting, marketing project implementation, or project management using Agile methodologies.
- Proven experience with Salesforce Data Cloud implementations.
- Strong consulting, marketing project implementation, or project management experience using Agile methodologies.
Data Lake Solutions
Amazon S3 (Simple Storage Service): Often used in conjunction with other AWS services like AWS Glue and Amazon Athena to create a data lake.
Azure Data Lake Storage: A Microsoft Azure solution that offers large-scale data storage and analytics capabilities.
Google Cloud Storage: Used with other Google Cloud services like BigQuery and Dataflow to create a data lake.
Apache Hadoop: An open-source solution that allows for the storage and processing of large amounts of unstructured data.
Databricks Lakehouse: Combines the features of a data lake and a data warehouse, based on Apache Spark.
Data Warehouse Solutions
Amazon Redshift: A fully managed cloud data warehouse by AWS, designed to analyze large-scale data.
Google BigQuery: A serverless and highly scalable data warehouse offered by Google Cloud.
Microsoft Azure Synapse Analytics: Formerly known as Azure SQL Data Warehouse, it offers massive data analytics capabilities.
Snowflake: A cloud-native data warehouse solution that offers separate storage and compute capabilities.
Teradata: A data warehouse solution that can be deployed on-premises or in the cloud.
Oracle Exadata: A high-performance data warehouse solution offered by Oracle.
Hybrid Solutions (Data Lakehouse)
Databricks: As mentioned earlier, Databricks offers a "Lakehouse" architecture that combines the benefits of data lakes and data warehouses.
Delta Lake: An open-source storage layer that brings ACID transaction reliability to data lakes.
