Principal Software Engineer, Salesforce, Data

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, Mule Soft, 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, Git Hub 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.

• 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, Mule Soft, 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, Git Hub 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.