Lead Analytics Engineer

Responsibilities

• 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 querying 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 data is always reliable for downstream consumption.
• Partner with cross-functional business leaders to translate complex operational requirements into high-impact, scalable data solutions.

Requirements

• 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.

Nice-to-Haves

• 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.