About the role

Lead AI Data Engineer to architect and build data pipelines for Salesforce's Marketing organization. Responsible for translating business needs into technical specifications and developing production-grade AI/ML systems.

Responsibilities

Design, build, and deploy production-grade AI/ML systems, with a preference for expertise in agentic systems, search/recommendation platforms, or data-intensive applications.
Extensive hands-on experience with LLMs, including fine-tuning, RAG, Context Engineering, Prompt Engineering, and deployment.
Develop systems enabling AI agents to reason, plan, and utilize external tools (APIs, databases).
Partner with marketing to define data requirements, build rapid POCs, and document specs.
Automate ETL in Snowflake and Big Query.
Construct DBT ETL pipelines for data ingestion and transformation.
Implement GIT CI/CD for DevOps.
Analyze metrics with business stakeholders to generate actionable insights.
Develop POC solutions for new marketing metrics to aid decision-making.
Design simple, repeatable, and reusable automation data frameworks.
Oversee BI and data warehouse initiatives for Marketing.
Review and validate designs against solution architecture and standards.
Collaborate internally and externally on data collection, analysis, and reporting.
Provide data support to Data Science teams for model production.
Work effectively with global teams (North America, EMEA, APAC).
Manage technical consultant(s).

Bachelor's in Computer Science or related field with 10+ years of progressive experience in data engineering, modeling, automation, and analytics.
Solid, hands-on expertise in AI/ML, specifically with Large Language Models (LLMs), including fine-tuning, RAG, Context Engineering, Prompt Engineering, and deployment.
Robust understanding and clear articulation of data engineering principles, database design, tools, and architecture.
Proven ability to manage multiple high-priority projects and meet deadlines.
Proactive capacity to identify and implement operational improvements.
Experience with Web analytics platforms and data sets (Google Analytics preferred).
Proficient in Snowflake, Google Big Query, Redshift, or CDP.
Solid experience with ETL technologies: DBT, IICS, and FiveTran.
10+ years of proficiency in SQL, Bash, and Python scripting.
7+ years of hands-on experience with Airflow, CI/CD (Jenkins/similar), and GitHub.
Substantial experience with AWS technologies: ECS, CloudWatch, and Lambda.
Prior experience mentoring junior team members in a customer-focused environment.
Experience collaborating closely with Analytics/Data Science teams.
Essential team-first mentality and excellent interpersonal skills.
Must proactively communicate status, identify risks, and drive results with minimal supervision.
Highly motivated self-starter, agile in adapting to shifting priorities, and a critical thinker for innovative solutions under stringent deadlines.