Data Engineering SMTS LMTS- Revenue Analytics

SalesForce-ad; Hyderabad; On-site; INR 4,000,000 - 6,500,000; Full time

Job summary

Salesforce Hyderabad is seeking a hands-on Lead Member of Technical Staff to drive data engineering across the core data platform and integration interfaces. You will own high‑impact data pipelines, mentor engineers, and ensure seamless plug‑in to downstream apps and enterprise workflows.

You bring deep Python/SQL expertise, knowledge of dbt, Snowflake, Airflow, and Node.js, with a strong focus on reliability, scalable design, and cross‑team collaboration with Salesforce ecosystems.

Qualifications

• 10–14+ years of hands-on experience in Data/Software/Platform engineering.
• Execution leadership on complex data projects from requirements to deployment.
• Bridge between core data platforms and web app teams; strong communication.

Responsibilities

• Lead design, execution, and delivery of end-to-end data pipelines.
• Define data ingestion, transformation, orchestration, storage patterns.
• Build API integrations, Node.js services, and middleware for apps.
• Implement agentic features and MCP server integrations.
• Establish testing strategies, CI/CD, and SLOs/SLAs for reliability.
• Mentor senior engineers and enforce engineering standards.

Skills

Python

SQL

dbt

Snowflake

Airflow

Node.js

TypeScript

REST APIs

Kubernetes

CI/CD

Tools

Docker

Kubernetes

Terraform

AWS

Job description

Job Description:

Description

About Salesforce

We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good– you’ve come to the right place.

Department Overview

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

Our team partners with business and technology leaders to solve complex challenges through modern data architecture, governed data products, integrated app services, and agentic systems. We emphasize production reliability, measurable business value, and responsible automation—not experimentation without operational accountability.

Role Description: Hands On Data Lead & Systems Integrator

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.

Core Responsibilities

• 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).

Qualifications

• 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 effective across engineering teams.