Data Engineer / Data Analyst (Supply Chain Data Ingestion)

Focus: Large-Scale Supply Chain Data Ingestion, Lakehouse Pipelines (Silver to Gold), and SSBI Enablement

Role Overview

Seeking a technical Data Engineer / Data Analyst to drive a major supply chain data platform initiative. This role focuses heavily on building robust ingestion pipelines for hundreds of tables originating from Warehouse Management Systems (WMS) and Geek+ supply chain automation systems.

You will engage with business stakeholders to gather requirements (defining target tables, logic, and filters), transform data from Silver to Gold layers within a Lakehouse architecture, and deliver clean data structures. Front-end dashboard design will be handled by dedicated developers, allowing this role to focus on core data engineering, pipeline construction, and data flows.

Core Responsibilities

• Pipeline Ingestion & Building: Design and construct scalable data ingestion pipelines for several hundred supply chain tables into the Lakehouse environment.
• Lakehouse Engineering (Silver to Gold): Transform raw/Silver supply chain data into optimized Gold-layer dimensional models to enable Self-Service BI (SSBI).
• WMS & Geek+ Integration: Technical focus on ingesting and modeling core WMS data alongside upcoming Geek+ supply chain automation datasets.
• Requirement Gathering: Partner with supply chain business teams to capture table structures, business rules, metrics, and filtering criteria.
• Data Flow & Semantic Preparation: Build data flows and semantic structures in Microsoft Fabric / Databricks to feed clean datasets to front-end Power BI developers.

Technical Stack & Requirements

• Experience: 4-6+ years of strong data engineering experience, specifically focused on pipeline build and ETL/ELT workflows.
• Lakehouse Platforms: Strong expertise in Databricks or Microsoft Fabric. High proficiency in Databricks with Lakehouse/Medallion architecture is fully acceptable if Fabric experience is lighter.
• Data Pipelines & Ingestion: Hands-on mastery of PySpark, Spark, and SQL for handling high-volume table ingestion and complex data transformations.
• Power BI & Data Flows: Good working knowledge of Power BI semantic layers and data flows to ensure smooth handoff to front-end UI developers.
• Domain Exposure: Background in supply chain, logistics, WMS, or automated inventory systems (e.g., Geek+) is highly preferred.