Lead Data Engineer Enterprise Reporting & Analytics

sephora.com•San Francisco, CA

1d•Hybrid

About The Position

Ready for a career glow up? As a Lead Engineer you will design and implement innovative analytical solutions and work alongside the product engineering team, evaluating new features and architecture. Reporting to the Director, Engineering, Data & AI you will work closely with other team members like data architects, data engineers, report developers, product and business analysts to understand what the business is trying to achieve, move data from source to target, and design optimal data models. You will be also responsible for building and maintaining reporting capabilities in the data platform that will feed data from the data models. This hands-on technical role demands excellency with AI/ML concepts and their integration into data engineering pipelines. Come be a part of a team that is starting this new journey.

Requirements

8+ years of experience in software development and deployment with business intelligence tools.
8+ years of experience in business intelligence and data warehousing, including mastery of SQL, Data Warehousing, and Big Data and stream processing.
2+ years of hands-on experience building or integrating AI/ML-powered features and agentic workflows into production applications.
1+ year of experience using AI-assisted development tools (e.g., Claude Code, Cursor, GitHub Copilot, Cline, Aider) to accelerate software delivery.
5+ years of experience analyzing large datasets to identify trends, patterns, and outliers to extract meaningful insights.
5+ years of experience writing design documentation, Source-to-Target mapping documentation, and managing Confluence pages.
5+ years of experience converting business functionalities into technical Jira stories.
3+ years of hands-on experience with SQL, Databricks, ADF, Datastage (or other ETL tools), SSAS Cubes, Cognos, Tableau, ThoughtSpot, and other BI tools.
Hands-on experience designing and building agentic workflows using orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or Amazon Bedrock Agents.
Experience implementing RAG (Retrieval-Augmented Generation) pipelines using vector databases (Pinecone, Weaviate, pgvector, Chroma) and frameworks (LlamaIndex, LangChain Retrieval).
Working knowledge of the Model Context Protocol (MCP) for connecting AI models to enterprise data sources, tools, and APIs.
Strong experience with data modeling, design patterns, and building highly scalable Business Intelligence solutions.
Demonstrated ability to mentor and coach engineering talent, including upskilling teams on AI-augmented development practices.
Excellent communication and storytelling skills, with the ability to clearly convey complex analytical results to business partners.
Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, Economics, Finance, or another quantitative field.

Responsibilities

Lead, design, and implement innovative analytical solutions using advanced SQL, Databricks, Tableau, Cognos, SSAS Cubes, ETL tools, and other Big Data technologies, while monitoring and optimizing BI system performance to ensure efficient data retrieval, processing, and reporting.
Drive data quality and cost efficiency by implementing quality control processes to ensure the accuracy of data, dashboards, and reports, while enforcing cost optimization strategies across the data platform to minimize compute and storage costs.
Perform detailed analysis of business problems and technical environments to design high-quality technical solutions, converting business functionalities into actionable technical strategy.
Drive the adoption of AI-assisted development tools (e.g., Claude Code, GitHub Copilot, Cursor) and automation platforms to enhance engineering productivity, solution quality, and agentic workflow initiatives.
Collaborate with business stakeholders, data analysts, developers, and IT teams to understand requirements and deliver solutions that meet their needs — translating complex technical concepts for non-technical audiences through effective communication.
Partner closely with Data Science, Machine Learning, and Analytics teams to provide data required for developing, operationalizing, and evaluating predictive models, dashboards, and reports.
Manage vendors to understand the potential architectural impact of different vendor strategies and ensure alignment with the overall data platform architecture.
Build and foster a high-performance engineering culture — mentoring and upskilling team members and providing the tools and motivation to make things happen.