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.
