Data Engineering SMTS LMTS

Description

The Experience

The Revenue Analytics team builds the data platforms, intelligent products, application capabilities, and AI-enabled automation that power enterprise decision-making. We combine deep engineering expertise with business context to deliver trusted, scalable systems for both human and agent-driven workflows.

We partner with business and technology leaders to solve complex challenges through modern data architecture, governed data products, and agentic systems, emphasizing production reliability, measurable business value, and responsible automation.

We are seeking a Lead Member of Technical Staff (LMTS) to be a hands-on technical execution lead across our core data platform and its application integration interfaces. Working within the architecture set by Principal Engineers, you will own delivery of high-impact data engineering projects, mentor senior engineers, and turn complex requirements into production-ready software.

What You'll Actually Be Doing

• Lead design, delivery and optimization of end-to-end batch and event-driven data pipelines, writing production code and guiding the team to ship scalable data products on schedule
• Define patterns and standards for data ingestion, transformation, orchestration, storage, data contracts and lineage using Python, SQL, dbt, Snowflake and Airflow
• Build API integrations, Node.js services and middleware that connect the data platform to applications, dashboards and enterprise platforms (Salesforce, AWS), including enterprise-controlled agentic workflows and MCP servers
• Establish testing, CI/CD, observability and SLOs/SLAs for reliability, and mentor Senior and Associate Engineers through code and design reviews
  
  

• 10 to 14+ years in Data, Software or Platform Engineering (or equivalent depth), with a proven record of leading complex data projects from requirements through deployment and operation
• Deep expertise in Python, advanced SQL, dbt, Airflow, Spark and Snowflake (including performance tuning and cost management), plus distributed systems, data modeling and Data Mesh concepts
• Practical experience with Node.js, TypeScript/JavaScript, REST APIs, GraphQL, webhooks and event queues (Kafka, AWS SQS/SNS), including OAuth2 and SSO
• Ability to bridge data platform and web application teams, and to translate technical choices into business outcomes with product managers and engineers
  
  

• Hands-on experience with AI and agentic tools such as LangGraph, LangChain, MCP servers, vector stores, and AI developer tools (Cursor, Codex, Claude Code)
• Experience operating on AWS, with Docker, Kubernetes/ECS, Lambda, CI/CD and Terraform
• Snowflake or AWS certifications
• Experience building data products that serve AI and agent-driven applications