Lead Analytics Engineer – Enterprise Data & AI

Team: IT Infrastructure

Location: Foster City, CA

Commitment: Full-time

Workplace Type: hybrid

Salary:

In this role, you will:

Design and maintain a unified semantic model that provides a single source of truth for cross-functional stakeholders, AI Agents, Self Serve Analytics and Executive dashboards.
Collaborate with Data & AI Engineers to structure and optimize the data that allows to query enterprise knowledge with high accuracy and low latency.
Establish organizational standards for data modeling, version control, testing, and documentation to ensure high data quality and system maintainability.
Implement automated testing and observability frameworks that proactively identify data anomalies and self-heal pipelines, ensuring our data is always reliable for downstream consumption.
Partner with cross-functional business leaders to translate complex operational requirements into high-impact, scalable data solutions.

Qualifications:

10+ years in Data Engineering & Analytics, with extensive hands-on experience building a semantic framework using Python, SQL and modern orchestration frameworks (e.g. Airflow, Lakeflow, Argo). With at least 2+ years of hands-on experience deploying AI generated code.
Extensive experience using modern data stacks (e.g. Snowflake/Databricks, Big Query) to build complex, enterprise-grade data models.
Deep understanding of data structures within large-scale enterprise platforms (SAP S/4HANA, Salesforce, Workday, etc.) and the ability to reconcile disparate schemas into clean models.
Exceptional ability to design modular, scalable, and performant data architectures that prioritize ease of use for downstream AI agents and Analytics tools.
A track record of driving technical projects from design to completion, mentoring junior engineers, and fostering a culture of collaboration and data excellence.

Bonus Qualifications:

Experience using LLMs to automate data reconciliation, anomaly detection or root-cause analysis within analytics pipelines with cloud-native data platforms (e.g., Snowflake, Databricks).
Expert-level Python & SQL skills with a focus on query optimization and performance tuning for massive datasets and reviewing AI generated code.
Proficiency in creating self-service Analytics environments (e.g., Tableau, Streamlit) that provide actionable insights to business stakeholders.