Employee Success Lead Data Engineer

Role overview

Lead Data Engineer (Employee Success) responsible for building and optimizing data pipelines, metrics/analytics solutions, and AI/GenAI solutions used by employees globally. You will partner with business teams to turn requirements into technical solutions and provide technical leadership across Salesforce data engineering capabilities.

Key missions

• Design, develop, and optimize data pipelines to support analytics, AI models, and generative AI applications.
• Architect and maintain APIs for smooth data exchange between systems, including Salesforce Data360 and Agentforce.
• Collaborate with business (e.g., sales) teams to translate requirements into technical solutions and integrate data from multiple sources.
• Implement data governance, security, and compliance best practices.
• Optimize data storage and processing and support data needs for data scientists.
• Provide technical leadership and drive cross-functional delivery of AI-driven solutions.

Responsibilities

• Build automated pipelines that generate actionable insights.
• Integrate data from multiple sources into analytics-ready datasets.
• Develop and maintain APIs for data interoperability.
• Leverage AI/ML capabilities, including model deployment.

Requirements

• Experience with data orchestration tools (e.g., Apache Airflow) and version control (Git).
• Deep understanding of data engineering concepts, database design, and data architecture.
• Proficiency with SQL and Python.
• Experience with Informatica IICS and dbt.
• Experience collaborating with Analytics/Data Science teams.
• Strong data warehousing and data modeling knowledge.
• Experience integrating systems through APIs.
• Strong AI/ML expertise including generative AI, LLMs, NLP, and AI model deployment.
• Proficiency with SQL, Bash, and Python scripting.
• Self-starter who can adapt to changing priorities and solve complex problems.

Preferred / nice-to-have

• Experience with Salesforce Data360 and Snowflake or similar platforms.
• AWS experience (e.g., EC2, Aurora, Lambda, S3).
• People Analytics experience.
• Experience designing AI-driven solutions including RAG, vector databases, and embedding models.

Education / experience

• Bachelor’s degree in Computer Science or relevant work experience.
• 10+ years in data engineering, data modeling, machine learning, automation, and analytics.