Senior Data Engineer - Omni Channel
Listed on 2026-02-05
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IT/Tech
Data Engineer, Data Science Manager
Overview
Here at Discount Tire, we celebrate the spirit of our people with extraordinary pride and enthusiasm. Our business has been growing for more than 60 years and now is the best time in our history to join us. We recognize that to remain the industry leader we must continue to grow and evolve our business in a rapidly changing industry. We are achieving this, not only by opening new stores, but by transforming our technological landscape and making data a central component of our strategy.
The Business Analytics team, one of the fastest growing teams in the company, is leading this change. We are responsible for driving the insights, recommendations, and developing the decision support tools that influence the strategic direction of the company.
Under minimal supervision, the Senior Data Engineer designs, builds, and operates scalable data solutions on Discount Tire's Databricks-based lakehouse platform. This role supports enterprise analytics, business intelligence, and data science initiatives by delivering high-quality, governed, and performance data products across Omni-channel domains. The Senior Data Engineer is responsible for developing and maintaining ELT pipelines, data models, and lakehouse architecture using modern cloud and big data technologies, with a strong focus on reliability, performance, and production ownership.
Essential Duties and Responsibilities- Lakehouse & Data Architecture
- Designs, develops, and maintains Databricks-based lakehouse solutions leveraging medallion architecture (Bronze, Silver, Gold) and Delta Lake.
- Builds scalable, high-performance analytical data models to support reporting, analytics, and downstream consumption.
- Ensures data solutions align with enterprise standards for security, governance, scalability, and cost efficiency.
- Data Engineering & ELT Pipelines
- Develops, tests, and maintains batch and streaming ELT pipelines using Spark SQL.
- Implements incremental data processing, dependency management, and idempotent pipeline patterns across multiple data sources.
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