Lead Data Engineer (Retail, Omnichannel)

Job summary

Design and implement scalable data ingestion, transformation, and data product frameworks using Databricks and Azure. Lead technical initiatives, establish engineering standards, and mentor team members to drive the modernization of the retail analytics platform.

Job details

We are hiring 2 Senior / Lead Data Engineers to help build the next generation of a Retail & Omnichannel Analytics platform. This is a hands-on engineering + technical leadership opportunity for engineers who have helped build or modernize enterprise-scale data platforms and can combine Databricks architecture, engineering best practices, and hands-on delivery. 🔹 What You’ll Do Design and implement scalable data ingestion, transformation, and data product frameworks. Establish and drive adoption of Databricks best practices, including Bronze/Silver/Gold (Medallion Architecture), governance, performance, data quality, and operational excellence. Build batch, near-real-time, and streaming pipelines using Databricks and Azure. Develop end-to-end data products supporting Retail & Omnichannel Analytics. Build trusted analytical datasets, dimensional/semantic models, and governed self-service analytics capabilities. Enable data democratization through Databricks Genie and reusable business-ready data products. Establish reusable frameworks, engineering standards, and platform patterns that can be adopted across multiple teams. Lead technical POCs and evaluate emerging capabilities across the Databricks ecosystem. Drive AI-assisted development, AI agents, and modern AI SDLC/engineering practices. Implement data quality, lineage, monitoring, observability, and governance. Partner with engineering, analytics, product, and business teams while mentoring engineers and influencing technical direction. 🔹 Must-Have Skills 7+ years of Data Engineering experience Strong, hands-on Databricks experience in enterprise production environments Advanced Python, SQL, Scala, Spark/PySpark Strong Delta Lake experience Lakehouse & Medallion Architecture (Bronze/Silver/Gold) Strong ETL/ELT, data integration, and scalable pipeline development Data modeling / dimensional modeling End-to-end analytics platform experience from ingestion → transformation → semantic layer → reporting Azure Data Factory (ADF) Azure DevOps, Git, CI/CD and release management Strong understanding of data governance, security, data quality, performance optimization, and observability ⭐ Highly Preferred Unity Catalog, LakeFlow, Delta Live Tables (DLT), Databricks SQL, Databricks Workflows Databricks Genie Experience establishing platform standards, reusable frameworks, and engineering best practices across multiple teams AI-assisted development / AI engineering Experience building AI agents or engineering automation Platform modernization / data transformation leadership Retail, Omnichannel, Merchandising, Inventory, Supply Chain, Store Operations, Customer Analytics, or Digital Commerce experience 🎯 Ideal Background We're particularly interested in engineers who have previously established or modernized Databricks/data platforms, rather than candidates whose experience is limited to maintaining individual data pipelines. The ideal candidate can think architecturally, define scalable patterns and standards, and still get hands-on with Python, PySpark, SQL, Scala, Databricks and Azure.

What you’ll do

Design and implement scalable data ingestion, transformation, and data product frameworks using Databricks and Azure. Lead technical initiatives, establish engineering standards, and mentor team members to drive the modernization of the retail analytics platform.

Requirements

Requires 7+ years of data engineering experience with strong hands-on expertise in Databricks, Python, SQL, and Spark. Candidates must have a proven background in building enterprise-scale data platforms and implementing Medallion architecture.

Listed skills

• SQL · Preferred
• CI/CD · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Databricks
• Python
• SQL
• Scala
• Spark
• PySpark
• Delta Lake
• Medallion Architecture
• Azure Data Factory
• Azure DevOps
• CI/CD
• Data Modeling
• ETL
• ELT
• Data Governance
• Data Quality

Job areas

• Data & Analytics
• Technology
• Engineering
• Software
• Retail