Data Engineering SMTS/LMTS- Revenue Analytics

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.

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.

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).