Data Engineering SMTS/LMTS- Revenue Analytics

Overview

Lead Member of Technical Staff (LMTS) in the Revenue Analytics team, serving as a hands-on technical execution lead across the core data platform and its application integration interfaces.

What you'll do

• 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.
• 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 business analytics and AI applications.
• 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).
• Implement enterprise-controlled agentic capabilities and AI-enabled workflows, integrating autonomous agents and Model Context Protocol (MCP) servers into application endpoints and data pipelines.
• Establish testing strategies, CI/CD pipelines, performance tuning, and operational observability across data pipelines and app integration services.
• Define and enforce SLOs/SLAs for pipeline availability, data freshness, and API endpoint performance.
• Conduct code and design reviews and mentor Senior and Associate Engineers on clean code, automated testing, and documentation.

What you'll need

• Typically 10 to 14+ years of hands-on experience in Data Engineering, Software Engineering, or Platform Engineering (or equivalent demonstrated technical depth).
• Deep expertise in Python and advanced SQL, with a strong background in operating production data platforms.
• Extensive experience with 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.
• 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).
• Practical experience building or integrating AI-enabled workflows using 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.
• 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).
• Proven track record of leading complex data projects from requirements through architecture, deployment, and ongoing operation.
• Ability to serve as the bridge between core data platform engineering (Python/SQL/Snowflake) and web application developer teams (Node.js/APIs).
• Ability to translate technical implementation choices into business outcomes, work closely with product managers, and communicate effectively across engineering teams.

Details

• Location: Hyderabad, India.