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MPA Recruitment

Sr Data Engineer

Toronto, ON, Canada

$110-120k · Full-time

Apply on LinkedIn About employer

Applications open in a new tab.

Job details

Pay: $110-120k
Job type: Full-time
Apply by: Dec 15, 2026
Posting language: English
Posted: Nov 15, 2026

Original job description

Sr Data Engineer

Toronto (hybird)

$110-120k

Global software consultancy

As a Senior Data Engineer, you will be responsible or designing and developing data processing and data persistence software components for solutions which handle data at scale. Working in agile teams, Senior Data Engineers provide strong development leadership and take responsibility for significant technical components of data systems. You will work within a multi-skilled agile team to design and develop large-scale data processing software to meet user needs in demanding production environments.

Your responsibilities will include working to develop data processing software primarily for deployment in Big Data technologies. The role encompasses the full software lifecycle including design, code, test and defect resolution. Working with Architects and Lead Engineers to ensure the software supports non-functional needs. Collaborating with colleagues to resolve implementation challenges and ensure code quality and maintainability remains high. Leads by example in code quality. Working with operations teams to ensure operational readiness. Advising customers and managers on the estimated effort and technical implications of user stories and user journeys. Coaching and mentoring team members

MINIMUM (ESSENTIAL) REQUIREMENTS:

Strong software development experience in one of Java, Scala, or Python

Software development experience with data-processing platforms from vendors such as AWS, Azure, GCP, Databricks.

Experience of developing substantial components for large-scale data processing solutions and deploying into a production environment

Proficient in SQL and SQL extensions for analytical queries

Solid understanding of ETL/ELT data processing pipelines and design patterns

Aware of key features and pitfalls of distributed data processing frameworks, data stores and data serialisation formats

Able to write quality, testable code and has experience of automated testing

Experience with Continuous Integration and Continuous Deployment techniques

DESIRABLE:

Experience of data pipeline development

Experience of Docker and Kubernetes

Experience of performance tuning

Experience of data visualisation and complex data transformations

Experience with steaming and event-processing architectures including technologies such as Kafka and change-data-capture (CDC) products

Expertise in continuous improvement and sharing input on data best practice

Apply on LinkedIn About employer

More about this job

Responsibilities

Design, develop, test, and maintain large-scale data processing and persistence components, primarily for big data platforms and production environments. Provide technical leadership, collaborate with architects and operations teams, advise on implementation effort, and coach and mentor team members.

Requirements

Candidates need strong software development experience in Java, Scala, or Python, along with experience building and deploying large-scale data processing solutions on cloud or data platforms. They should be proficient in analytical SQL, ETL/ELT design, distributed data technologies, automated testing, and CI/CD.

Skills

Java
Scala
Python
SQL
Data Processing
ETL/ELT
Distributed Data Processing
AWS
Azure
Google Cloud Platform
Databricks
Automated Testing
Continuous Integration
Continuous Deployment
Docker
Kubernetes

Visa sponsorship

Not detected in the job text

Keywords

Java
Scala
Python
SQL
Analytical Queries
ETL/ELT
Data Processing
Data Persistence
Big Data
Distributed Data Processing
Data Stores
Data Serialization
AWS
Azure
Google Cloud Platform
Databricks
Automated Testing
Continuous Integration
Continuous Deployment
Docker
Kubernetes
Performance Tuning
Data Visualization
Complex Data Transformations
Kafka
Change Data Capture
Agile
Production Deployment
Code Quality
Data Pipelines

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