Romania Job Openings
OSF Digital
Data Cloud Consultant
October 18, 2024
Location
Romania
Ukraine
Department
Salesforce Marketing Cloud
Salesforce Marketing Cloud
Location
Romania, Ukraine
Romania, Ukraine
Posted
October 18, 2024
October 18, 2024
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:
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 Big Query 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 Big Query: 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.
- 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.
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 Big Query 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 Big Query: 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.
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