Lead Analytics Engineer
Lead the design and implementation of a unified semantic layer and data models from complex enterprise systems (SAP, Salesforce, Workday) to create AI-ready datasets that power intelligent agents, analytics, and decision-making. Requires 10+ years data engineering experience, semantic modeling expertise, and hands-on AI-generated code deployment.
About the job
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.
Skills
Python, SQL, Airflow, Lakeflow, Argo, Snowflake, Databricks, BigQuery, Sap S/4Hana, Salesforce, Workday, LLMs, Tableau, Streamlit
Similar jobs
Data Engineering jobs
Zoox
Foster City, CA
Leads the architecture, scaling, security, governance, and cost optimization of enterprise and AI data platforms. Requires 10+ years of data or software engineering experience, with expertise in production data foundations, CI/CD, governance, security, and performance optimization.
Garner Health
New York, NY
Build and scale data pipelines, reusable datasets, and validation frameworks supporting business intelligence, marketing, and data science. The role requires strong Python and SQL skills, modern data-stack experience, and at least four years of software or data engineering experience.
Prompt Health
United States
Own the reliability, performance, observability, scalability, and cost efficiency of large Aurora MySQL production environments supporting healthcare applications. The role requires 6+ years of database engineering experience, deep MySQL and AWS expertise, and strong skills in automation, incident response, and query optimization.
OnePay
United States
Leads an analytics engineering team that transforms raw data into reliable, actionable insights for product, marketing, and operations. The role requires 7+ years in data or analytics engineering, management experience, and advanced SQL, Databricks, and dbt expertise.
Decagon
San Francisco, CASenior Data Infrastructure Engineer responsible for building and operating reliable, low-latency streaming and batch data systems that support AI products. Requires 5+ years of production data infrastructure experience and expertise with technologies such as Kafka, Flink, ClickHouse, and Terraform.