Data Engineer - MTS/SMTS/LMTS

Job Description

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Software Engineering Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. About the Role We are seeking a highly skilled and motivated Data Engineer to join our Growth & Retention Intelligence team. In this role, you will design, build, and scale the data that power our machine learning ecosystem – enabling consistent, reliable, and real-time access to features across development, training, and production environments. You’ll collaborate closely with data scientists, ML engineers, and data platform teams to streamline feature engineering workflows and ensure seamless integration between offline and online data sources. You’ll be expected to work across multiple domains including data architecture, distributed systems, software engineering, and MLOps. You will help define and implement best practices for feature registration, drift, governance, lineage tracking, and versioning, all while contributing to the CI/CD automation that supports feature deployment across environments. What You’ll Do Key Responsibilities: Feature Store Development: Implement and maintain scalable features serving offline (batch), online (real-time), and streaming ML use cases. Streaming & Real-Time Data Processing: Design and manage streaming pipelines using technologies like Kafka, Kinesis, or Flink to enable low-latency feature generation and real-time inference. Feature Governance & Lineage: Define and enforce governance standards for feature registration, metadata management, lineage tracking, and versioning to ensure data consistency and reusability. Collaboration with ML Teams: Partner with data scientists and ML engineers to streamline feature discovery, definition, and deployment workflows, ensuring reproducibility and efficient model experimentation. Data Pipeline Engineering: Build and optimize ingestion and transformation pipelines that handle large-scale data while maintaining accuracy, reliability, and freshness. CI/CD Automation: Implement CI/CD workflows and infrastructure-as-code to automate feature store provisioning and feature promotion across environments (Dev → QA → Prod). Monitoring & Observability: Develop monitoring and alerting frameworks to track feature data quality, latency, and freshness across offline, onlin

Required Skills