Posted 20 days ago
Lead Data Engineer
@ Flow Global Technology
Vancouver, British Columbia
Overview
Flow Global Technology is hiring a Lead Data Engineer on a contract basis to design and build scalable batch and streaming data pipelines and data platforms. The role covers data services and APIs, data modeling, data quality and observability, and mentoring data engineers. Candidates need strong Java, Spark, Kafka and Spring Boot experience.
Responsibilities:
- Design and develop batch and streaming data pipelines with Java, Spark and Kafka
- Architect data platforms across ingestion, storage and serving
- Build data services and APIs with Spring Boot
- Mentor and coach data engineers
Requirements summary:
- Experience leading data platform and pipeline design
- Strong Java, Spark and Kafka skills
- Knowledge of data governance and PIPEDA compliance
- Agile delivery experience
Tools & skills
Source and dates
Source type not verified · apify-linkedin
- Employer posting date
- 2026-10-21
- 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 | Lead |
| Department | Software development |
| Work setting | Office |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
Summary and details written by AI from the posting; the employer’s posting is the authority.
Flow Global Technology
| Field | Value |
|---|---|
| Open jobs on Jobsy | 1 |
Key Responsibilities
- Lead the design and development of scalable batch and streaming data pipelines using Java, Spark, and Kafka
- Architect data platforms covering ingestion, transformation, storage, and serving layers
- Build and maintain data services and APIs using Spring Boot
- Define data models, partitioning strategies, and schema management for data lake and warehouse environments
- Own code quality through code reviews, design reviews, and engineering best practices
- Mentor and coach data engineers, and support their technical growth
- Optimize pipeline performance, cost, and JVM/Spark tuning
- Implement data quality checks, monitoring, alerting, and observability
- Drive CI/CD, automated testing, and infrastructure-as-code practices
- Lead root-cause analysis for production data incidents
- Ensure data security, governance, and privacy compliance (including PIPEDA requirements)
- Contribute to estimation, sprint planning, and delivery in an Agile environment
Journal
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