Employee Success Lead Data Engineer

Salesforce•Indianapolis, IN

About The Position

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. Salesforce’s Employee Success (ES) Product Management team is advancing AI-driven data initiatives to expand our data capabilities across all ES functions. A key priority is developing a robust data and AI foundation to support innovation, automation, and data-driven decision-making. We are seeking a highly skilled Lead Data Engineer with expertise in data engineering, AI, and machine learning, along with experience in API development. Familiarity with Salesforce Data360 and Agentforce is a plus. In this role, you will build data pipelines, metrics, analytics solutions, and Agentic AI solutions used by employees globally. You will partner with business teams to translate requirements into technical solutions, integrate data from multiple sources, and develop automated pipelines that generate actionable insights.

Requirements

Bachelor’s degree in Computer Science or relevant work experience
10+ years in data engineering, data modeling, machine learning, automation, and analytics.
Proficiency with SQL, Python, Informatica IICS, and dbt.
Strong expertise in AI/ML, including generative AI, LLMs, NLP, and AI model deployment.
Experience designing AI-driven solutions, including RAG, vector databases, and embedding models.
Proficiency in SQL, Bash, and Python scripting.
Experience with orchestration tools (e.g., Apache Airflow) and version control (GIT).
Solid understanding of data warehousing and data modeling concepts.
Experience integrating systems through APIs.
Deep understanding of data engineering concepts, database design, tools, and architecture.
Experience collaborating with Analytics/Data Science teams.
Excellent interpersonal skills; team-first mentality.
Self-starter, highly motivated, able to adapt quickly to shifting priorities and solve complex problems.
Results-oriented, able to work with minimal supervision.

Nice To Haves

People Analytics experience is a plus.
Experience with AWS technologies (EC2, Aurora, Lambda, S3) preferred.
Experience with Salesforce Data360 and Snowflake or similar platforms preferred.

Responsibilities

Lead design, development, and optimization of data pipelines to support analytics, AI models, generative AI applications, and business intelligence.
Architect and maintain APIs for seamless data exchange between systems, including Salesforce Data360 and Agentforce, enabling AI-powered workflows.
Leverage AI and machine learning to enhance data processing, predictive analytics, and automation.
Implement and integrate large language models (LLMs) and generative AI solutions to improve data insights, automated decision-making, and user experiences.
Provide technical leadership in Salesforce technologies, including APEX code development, Salesforce Flow, and integrations with external data sources and AI services.
Collaborate cross-functionally to implement data governance, security, and compliance best practices.
Optimize data storage, processing, and retrieval in cloud environments, ensuring performance and scalability.
Provide guidance on Salesforce Data360 integration and utilization.
Partner with ES teams to define metrics, build proof-of-concepts, and document functional and technical requirements.
Design and develop repeatable automation frameworks supporting AI model training and inference.
Lead review and validation of logical and physical design to align with solution architecture.
Collaborate with data scientists to support data needs and deploy scalable models.
Lead and collaborate with global teams across North America, EMEA, and APAC.