Posted 7 days ago
Contract Data Engineer
@ SapienX Inc.
Toronto, Ontario
Overview
SapienX Inc. is hiring a contract Data Engineer for a 6-month consulting engagement supporting its clients. The role covers Azure data engineering, production support, pipeline enhancements and platform evolution on an established enterprise data platform. The posting asks for hands-on Azure, Databricks and PySpark work with immediate contribution and no extended ramp-up.
Responsibilities:
- Support and troubleshoot production data pipelines
- Enhance reliability and evolve the enterprise data platform
- Build and maintain Azure Data Factory, Databricks and PySpark workloads
- Deploy changes through DEV, QA and PROD
Requirements summary:
- Experience with Azure Data Factory and Azure Databricks
- Strong PySpark, Python and SQL skills
- Production troubleshooting and incident resolution
- Understanding of CI/CD and structured deployment practices
- Experience with lakehouse and medallion architectures
Tools & skills
Source and dates
Source type not verified · apify-linkedin
- Employer posting date
- 2026-11-03
- First seen by our source
- 2026-11-10
- Source record updated
- 2026-11-10
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Job details
| Seniority | Mid level |
| Department | Software development |
| Work setting | Office, Client sites |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
| Languages | English |
Summary and details written by AI from the posting; the employer’s posting is the authority.
SapienX Inc.
| Field | Value |
|---|---|
| Open jobs on Jobsy | 1 |
SapienX, Inc. is looking for an experienced Data Engineer for a 6-month consulting engagement supporting our clients.
We’re looking for someone who can hit the ground running in an established enterprise data environment and contribute immediately across both day-to-day platform needs and ongoing enhancements.
This isn't a role where you'll spend your first few months designing something that may eventually go into production. You'll be working with real enterprise data pipelines and production workloads, solving problems, improving reliability, and helping an experienced data engineering team continue evolving the platform.
What you'll be working withYou'll want to be very comfortable with:
- Azure Data Factory
- Azure Databricks and Lakehouse architectures
- PySpark and Python
- Azure Logic Apps
- SQL and complex data transformations
- Production support, troubleshooting, monitoring, and incident resolution
- CI/CD and structured DEV → QA → PROD deployment practices
Our architecture is moving toward a governed medallion model spanning SAP Business Data Cloud/Datasphere, Azure Databricks and ADLS, Microsoft Fabric/OneLake, Unity Catalog, and Power BI, so experience working across modern enterprise lakehouse patterns would be particularly relevant.
Who we're looking forThis opportunity is especially well suited to a mid-to-senior or senior Data Engineer who:
- Enjoys getting hands-on with real production data problems
- Can troubleshoot an unfamiliar pipeline and systematically get to root cause
- Is comfortable both supporting what exists and improving it
- Writes clean, maintainable Python/PySpark and SQL
- Understands how enterprise data solutions move safely through development, QA, and production
- Works collaboratively and communicates clearly with other engineers and stakeholders
- Can operate effectively without needing extensive ramp-up or day-to-day direction
Experience in any of these areas would be a strong plus:
- SAP data integration
- SAP Business Data Cloud (BDC)
- SAP Datasphere
- Microsoft Fabric
- Power BI data models
- Unity Catalog
- Azure SQL / Cosmos DB
- Modern medallion/lakehouse architecture
Duration: 6 months
Engagement: Contract through SapienX, Inc.
Focus: Azure data engineering, production support, pipeline enhancements, and platform evolution
If you're the kind of engineer who enjoys opening a failing pipeline, figuring out why it failed, fixing it properly, and then asking "how do we make sure this doesn't happen again?", I'd especially like to hear from you.
Interested? Send me a message on LinkedIn with a short introduction and your resume.
#DataEngineering #Azure #Databricks #PySpark #Python #AzureDataFactory #SQL #MicrosoftFabric #Lakehouse #SAP #ContractJobs
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