Data Engineering SMTS/LMTS- Revenue Analytics

Role details

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Software Engineering Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. About Salesforce We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good– you’ve come to the right place. Department Overview The Revenue Analytics team builds the data platforms, intelligent products, modern application capabilities, and AI-enabled automation that power enterprise decision-making. We combine deep engineering expertise with business context to deliver trusted, scalable systems that support both human and agent-driven workflows. Our team partners with business and technology leaders to solve complex challenges through modern data architecture, governed data products, integrated app services, and agentic systems. We emphasize production reliability, measurable business value, and responsible automation—not experimentation without operational accountability. Role Description: Hands On Data Lead & Systems Integrator We are seeking a Lead Member of Technical Staff (LMTS) to serve as a primary technical execution lead across our core data platform and its application integration interfaces. This is a hands-on technical leadership role. While operating within the overarching platform architecture established by Principal Engineers, you will own the delivery of high-impact data engineering projects, build resilient pipelines, and ensure data products seamlessly plug into downstream applications, web tools, and agentic workflows. You will bring deep expertise in core data engineering (Python, Snowflake, dbt, SQL) complemented by solid working knowledge of the app development ecosystem (Node.js, APIs, microservices) to bridge data platforms with full-stack systems. You will lead project-level execution, mentor senior engineers, and translate complex requirements into production-ready software. Core Responsibilities Data Engineering Project Leadership & Execution: Lead the design, execution, and optimization of end-to-end data pipelines for batch and event-driven workloads. Own project-level technical delivery, write high-performance production code, and guide team members to deliver scalable data products on schedule. Data Platform & Product Design: Define patterns for data ingestion, transformation, orchestration, and storage using Python, SQL, dbt, Snowflake, and Airflow. Establish standards for data contracts, semantic consistency, lineage, and reusable data assets to support both business analytics and AI applications. App Ecosystem & Integration Layering: Design and maintain lightweight API integration components, Node.js services, and middleware that expose data platform capabilities to full-stack applications, web dashboards, and external enterprise platforms (Salesforce, AWS). Agentic Architecture & Tool Integration: Implement enterprise-controlled agentic capabilities and AI-enabled workflows. Integrate autonomous agents and Model Context Protocol (MCP) servers into application endpoints and data pipelines, ensuring structured outputs, context retrieval, and clear human-agent handoffs. Verification & Production Reliability: Establish testing strategies, CI/CD pipelines, performance tuning, and operational observability across both data pipelines and app integration services. Define and enforce SLOs/SLAs for pipeline availability, data freshness, and API endpoint performance. Cross-Team Leadership & Quality Standards: Conduct code and design reviews. Mentor Senior and Associate Engineers, championing engineering standards for clean code, automated testing, and documentation across both data and application interfaces. Technical Profile Core Data Engineering Foundations: Deep expertise in Python and advanced SQL , with a strong background in operating production data platforms. Extensive experience with data stack technologies: dbt , Apache Airflow , Apache Spark , and Snowflake (including query optimization, performance tuning, cost management, and complex troubleshooting). Command of distributed systems, dimensional/domain-oriented data modeling, batch/streaming patterns, and Data Mesh / data-as-a-product concepts. Application Ecosystem & Integration Knowledge: Practical experience with Node.js and TypeScript / JavaScript for building lightweight backend services, microservices, or integration layers. Hands-on knowledge of designing and consuming REST APIs, GraphQL, webhooks, and event queues (Kafka, AWS SQS/SNS). Understanding of web app integration patterns, API gateways, and enterprise authentication protocols (OAuth2, SSO). AI & Agentic Engineering: Practical experience building or integrating AI-enabled workflows using frameworks like LangGraph or LangChain . Working knowledge of Model Context Protocol (MCP) servers, structured outputs, context engineering, vector stores, and retrieval mechanisms. Effective use of AI-native developer tools (