Hello, I hope this email finds you well. My name is Aalind Gargav and I am a Technical Recruiter at Empower Professionals Inc. We have a Data Engineer (Agentforce) role with our client located in SF, Seattle, Dallas, Indianapolis, New York If you have a matching candidate please send over a resume, after which I will give you a phone call to discuss further. Role: Data Engineer (Agentforce) Duration: 12 Months Work location: SF, Seattle, Dallas, Indianapolis, New York (other Salesforce office cities potentially) (Hybrid – 3 days)
Job Description: Must have – DBT (data build tool) experience
Key Skills: • Develop DBT ETL pipelines for data ingestion and transformation. • Maintaining, deploying and code versioning the ETL process. • Using GIT CI/CD for DevOps. • Actively develop, enhance and maintain data pipelines and workflows for marketing data and metrics. • Design & develop easy, repeatable and reusable automation data frameworks. • Work and collaborate with global teams across North America, EMEA and APAC. • Help in building POC solutions for new marketing metrics that drive effective decision making. • Design & develop easy, repeatable and reusable automation data frameworks. • Responsible for end-to-end data management activities, including but not limited to identify fields, data lineage and integration, performing data quality checks, analysis and presenting data.
Key Responsibilities & Scope: • Build metrics from unstructured data – Chat transcripts, AI Agent product logs, Agent interaction texts – to build conversational analytics. • Build metrics from structured data – Salesforce Data Cloud, Snowflake tables, Salesforce Sales Cloud objects. • Design, develop, and maintain data transformation pipelines using dbt to transform raw data into structured, usable datasets for analytics and reporting. • Collaborate with cross-functional teams including data analysts, software engineers, and product managers to translate business requirements into data-driven solutions that provide actionable insights and enhance customer experiences. • Optimize data storage, retrieval, and processing to support high-performance analytics, ensuring the system scales effectively with increasing data volumes. • Ensure data quality and consistency by implementing automated monitoring, alerting, and validation processes. • Automate and streamline data operations to reduce time-to-insight and enhance data reliability • Define and uphold data engineering best practices, including code quality, version control, and testing standards. • Support and strengthen data security, privacy, and compliance across all data engineering initiatives to meet industry standards
Job Description: Must have – DBT (data build tool) experience
Key Skills: • Develop DBT ETL pipelines for data ingestion and transformation. • Maintaining, deploying and code versioning the ETL process. • Using GIT CI/CD for DevOps. • Actively develop, enhance and maintain data pipelines and workflows for marketing data and metrics. • Design & develop easy, repeatable and reusable automation data frameworks. • Work and collaborate with global teams across North America, EMEA and APAC. • Help in building POC solutions for new marketing metrics that drive effective decision making. • Design & develop easy, repeatable and reusable automation data frameworks. • Responsible for end-to-end data management activities, including but not limited to identify fields, data lineage and integration, performing data quality checks, analysis and presenting data.
Key Responsibilities & Scope: • Build metrics from unstructured data – Chat transcripts, AI Agent product logs, Agent interaction texts – to build conversational analytics. • Build metrics from structured data – Salesforce Data Cloud, Snowflake tables, Salesforce Sales Cloud objects. • Design, develop, and maintain data transformation pipelines using dbt to transform raw data into structured, usable datasets for analytics and reporting. • Collaborate with cross-functional teams including data analysts, software engineers, and product managers to translate business requirements into data-driven solutions that provide actionable insights and enhance customer experiences. • Optimize data storage, retrieval, and processing to support high-performance analytics, ensuring the system scales effectively with increasing data volumes. • Ensure data quality and consistency by implementing automated monitoring, alerting, and validation processes. • Automate and streamline data operations to reduce time-to-insight and enhance data reliability • Define and uphold data engineering best practices, including code quality, version control, and testing standards. • Support and strengthen data security, privacy, and compliance across all data engineering initiatives to meet industry standards