Principal Software Engineer, Salesforce & Data

Salesforce•San Francisco, CA

About The Position

Salesforce is building the next generation of intelligent customer and business platforms by combining CRM, enterprise data, automation, and AI. As part of this vision, we are investing in a unified engineering ecosystem that spans Salesforce applications, Data 360, customer data platforms, enterprise integrations, and AI-powered developer experiences. We are seeking a highly motivated, hands-on Principal Member of Technical Staff (PMTS) who brings a rare combination of software engineering, data engineering, and AI expertise. This role is ideal for a technical leader who can seamlessly operate across CRM applications, enterprise integrations, data platforms, and modern AI-powered development environments. You will help define the technical strategy for critical business capabilities while building scalable systems that connect transactional applications with enterprise data platforms. Just as importantly, you will be a force multiplier for engineering productivity by leveraging modern AI development platforms to accelerate software delivery and developer efficiency.

Requirements

12+ years of experience across software engineering, platform engineering, data engineering, or enterprise application development.
Strong hands-on experience building applications on Salesforce technologies including Apex, Lightning Platform, CRM, APIs, MuleSoft, and Heroku.
Strong experience building enterprise-scale data platforms, data products, data pipelines, and data-intensive applications.
Deep expertise in Python, SQL, distributed data processing, orchestration frameworks, and modern data engineering practices.
Experience designing and operating large-scale ETL/ELT pipelines using Airflow or similar orchestration technologies.
Proven experience integrating application platforms with enterprise data ecosystems using APIs, streaming technologies, event-driven architectures, and cloud-native services.
Demonstrated experience building AI-powered developer tools, engineering productivity platforms, or software delivery accelerators using modern AI platforms such as Claude Code, Cursor, Windsurf, GitHub Copilot, or equivalent technologies.
Strong understanding of Generative AI, agentic workflows, MCP frameworks, and practical applications of AI within enterprise software engineering organizations.
Ability to influence technical direction across multiple teams and organizations while remaining deeply hands-on.
Proven ability to take complex technical initiatives from concept through production deployment.

Nice To Haves

Experience with Salesforce Data Cloud (Data360), Customer 360, Identity Resolution, Data Modeling, Segmentation, Activations, or Customer Data Platforms.
Experience building marketing technology, personalization, customer engagement, or campaign management applications.
Experience with Snowflake, Databricks, Kafka, CDC frameworks, and modern cloud data platforms.
Experience building customer identity, profile, account hierarchy, master data, or knowledge graph solutions.
Experience with Retrieval-Augmented Generation (RAG), vector databases, AI agents, semantic search, or AI-native application architectures.
Experience building internal developer platforms, engineering productivity systems, CI/CD automation, or product engineering solutions.
Experience working across both application engineering and data engineering domains at enterprise scale.

Responsibilities

Define and drive technical strategy across Salesforce CRM, Data 360, enterprise data platforms, and AI-enabled engineering initiatives.
Architect and build large-scale applications using Salesforce Core, Apex, Lightning Platform, MuleSoft, Heroku, APIs, and cloud-native services.
Design and implement enterprise data products, data pipelines, and data services supporting customer, account, marketing, and operational use cases.
Build scalable integrations between Salesforce applications, Data 360, Snowflake, enterprise data warehouses, and external systems using APIs, streaming, and event-driven architectures.
Design and develop AI-powered engineering accelerators, developer tools, code generation frameworks, testing automation solutions, and operational tooling using Claude Code, Cursor, Windsurf, GitHub Copilot, and agentic AI technologies.
Partner closely with Product Managers, Architects, Data Engineers, and Software Engineers to define platform roadmaps and long-term architectural direction.
Drive the design of highly scalable data processing frameworks leveraging Airflow, Python, SQL, streaming technologies, CDC patterns, and modern cloud-native architectures.
Establish engineering standards for data quality, observability, platform reliability, governance, and operational excellence.
Mentor senior engineers and provide technical leadership across multiple teams and business domains.
Lead complex production investigations spanning applications, integrations, data pipelines, and platform services.