Data Engineering SMTS/LMTS- Revenue Analytics
Salesforce · Posted Oct 16
Customer relationship management, cloud computing, enterprise software, and artificial intelligence solutions
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About the role
The Revenue Analytics team builds the data platforms, intelligent products, modern application capabilities, and AI-enabled automation that power enterprise decision-making, combining deep engineering expertise with business context to deliver trusted, scalable systems for human and agent-driven workflows. The team works across modern data architecture, governed data products, integrated app services, and agentic systems with an emphasis on production reliability and responsible automation. This hands-on Lead Member of Technical Staff role leads delivery of high-impact data engineering projects, builds resilient batch and event-driven pipelines, and integrates data products with full-stack applications, APIs, and agentic workflows.
What you will do
- 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.
Skills used in this role
What the employer is looking for
- 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 (e.g., Cursor, Codex, Claude Code) with rigorous review and testing of generated code.
- Cloud & DevOps Infrastructure: Hands-on experience operating on cloud platforms, preferably AWS.
- Familiarity with Docker, container orchestration (Kubernetes/ECS), serverless deployment (Lambda), CI/CD pipelines, and Infrastructure as Code (Terraform).
- Experience: Typically 10 to 14+ years of hands-on experience in Data Engineering, Software Engineering, or Platform Engineering (or equivalent demonstrated technical depth).
- Execution Leadership: Proven track record of leading complex data projects from requirements through architecture, deployment, and ongoing operation.
- Hybrid Systems Knowledge: Ability to serve as the bridge between core data platform engineering (Python/SQL/Snowflake) and web application developer teams (Node.js/APIs).
- Business & Communication Skills: Ability to translate technical implementation choices into business outcomes, work closely with product managers, and communicate effectively across engineering teams.
Benefits and support
- Compensation and benefits are detailed in the job posting
About Salesforce
Salesforce, Inc. is a leading global cloud-based enterprise software company specializing in customer relationship management (CRM) and artificial intelligence solutions. Through its integrated Customer 360 platform, the company provides applications for sales, customer service, marketing automation, e-commerce, and analytics. Powered by innovations like Agentforce, Salesforce helps businesses bridge human capability with autonomous AI agents to drive customer success.
- Industry
- Cloud Computing
- Company size
- 87000+ employees
- Founded
- 1999
- Location
- San Francisco, California, USA
- Funding stage
- Public Company
Funding
Public Company · $25B raised
- 2026-03Post-IPO Debt$25B
- 2003-01Venture Round$1M
- 2001-06Series D$46.9M
- 1999-11Series C$13.2M
- 1999-06Series B$3.8M
Leadership
Chair, Chief Executive Officer & Co-Founder
President & Chief Operating and Financial Officer
Co-Founder & Chief Technology Officer, Slack
President & Chief Strategy Officer
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