Full-Stack Data Engineer

About the Role

Campus Activewear is scaling a cloud data platform that consolidates multi-department data into a reliable lakehouse for analysis and decision-making.

We need a hands-on Full-Stack Data Engineer who can own the data pipeline layer end-to-end, partner with IT (who own infrastructure), reverse-engineer and stabilize existing vendor-built work, and also build Azure-hosted web applications on top of trusted data.

This is not a prompt AI and ship notebooks” role. You must be able to design, debug, document, and operate production pipelines yourself.

What You Will Own:

Data platform (primary)

• Stabilize, document, and improve existing Azure Data Factory ADLS Databricks (bronze / silver / gold) pipelines
• Onboard new source systems into the medallion architecture
• Improve gold-layer reliability (especially consolidated sales / channel registers)
• Own failed-pipeline triage and on-call for production breaks
• Work with IT on infra (networking, VMs/SHIR, access, secrets) while owning pipeline logic, data quality, and delivery
• Take over / reverse-engineer vendor-delivered ADF pipelines and Databricks notebooks

Web applications (secondary but real)

• Build internal tools on Azure App Service — dashboards, workflows, data-entry portals, and APIs over gold tables
• Stack is open (Python/FastAPI, React/Next, .NET, etc.) — choose pragmatically for Campus needs

Business partnership

• Translate Sales / ops / channel requirements into data models and usable apps
• Balance stakeholder communication with deep engineering delivery

Must-Have Skills

• Strong fundamentals in ETL/ELT, lakehouse / medallion patterns, SQL, and Python
• Hands-on experience with at least one cloud data stack at production scale — prefer Azure + Databricks (ADF, ADLS/Delta, notebooks/jobs); also strong: Synapse/Fabric, Snowflake + orchestration, AWS Glue/EMR + S3 if ramp to Azure/Databricks is fast
• Can explain a past pipeline source landing clean curated consumer without relying on AI-generated answers
• Comfort with production operations: retries, monitoring, failure diagnosis, incremental loads / MERGE concepts, data quality checks
• Ability to build and deploy simple-to-moderate web apps / APIs on Azure
• Clear documentation habits (runbooks, pipeline maps, data contracts)

Strong Preference

• Retail / CPG / footwear / multi-channel sales domain
• SAP extraction / connectors / sales data familiarity
• Unity Catalog, Delta Lake, service principals / Key Vault (or equivalent elsewhere)
• Experience inheriting messy vendor codebases and making them operable

What We Are Not Looking For

• Pure Power BI / analyst profiles with light “data eng” exposure
• Candidates who can only describe architecture slides, not debug a failed run
• Frontend-only or backend-only engineers with no data pipeline experience
• People who need a large data team around them to be effective