Data Engineering SMTS/LMTS

• 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 (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).