Senior Data Platform Engineering Specialist

Category: Infrastructure/Cloud

Main location: Canada, Ontario, Toronto

Position ID:J0826-0688

Employment Type: Full Time

Position Description:

Location: Toronto/Montreal Preferred
Languages: Bilingual (English/French) Preferred
Employment Type: Full-Time

CGI is seeking an experienced Senior Data Platform Engineering Specialist to join our growing Data Platform Engineering consulting practice within the Emerging Technologies team.

As a full-time member of CGI, you will become part of a collaborative team of architects, engineers, and consultants delivering enterprise technology solutions for some of Canada's leading public and private sector organizations. While you may be assigned to one or more client engagements, you will remain a permanent member of CGI's Data Platform Engineering practice, collaborating with teammates, contributing to reusable data assets, and continuously developing your technical and consulting expertise.

In this role, you will help clients modernize how they ingest, store, process, govern, and analyze data. You will design, implement, and support modern data platforms (Data Lakes, Data Warehouses, and Lakehouses) that improve data democratization, operationalize machine learning models, and drive business intelligence.

Success in this role requires curiosity, adaptability, strong data engineering fundamentals, and a passion for continuous learning. Rather than specializing strictly in a single technology stack, you will leverage sound DataOps principles to deliver innovative, scalable, and secure data infrastructure across a variety of industries, technologies, and client environments.

Your future duties and responsibilities:

Data Platform Engineering

Design, build, and support scalable data platforms that enable batch and real-time data processing, analytics, and AI workloads.

Implement and manage modern data architectures, including Lakehouses (e.g., Databricks), Cloud Data Warehouses (e.g., Snowflake), and robust ETL/ELT pipelines.

Develop reusable data platform capabilities that enable self-service data ingestion, transformation, and exploration for downstream analysts and data scientists.

Optimize data storage, compute scaling, and query performance across large-scale distributed data systems.

DataOps & Automation

Design and implement modern data delivery pipelines using CI/CD, Data as Code, and automation.

Automate the provisioning of data infrastructure, workspace configuration, and data pipeline orchestration (e.g., using Apache Airflow or dbt).

Improve data quality, pipeline reliability, operational stability, and engineering efficiency through automated testing and continuous improvement.

Data Governance & Security

Implement robust data governance frameworks, fine-grained access controls, and data lineage tracking (e.g., Unity Catalog).

Integrate data security best practices throughout the data lifecycle, ensuring compliance, data masking, and secure data sharing.

Support incident response for data pipelines, data quality root cause analysis, and capacity planning.

Cloud & Infrastructure Engineering

Design, deploy, and support secure data solutions across public cloud environments (AWS, Azure, GCP).

Implement foundational data infrastructure using Infrastructure as Code (IaC) such as Terraform.

Apply cloud networking, identity, resiliency, and cloud cost optimization (FinOps) best practices for data workloads.

Innovation & Emerging Technologies

Evaluate emerging data technologies, table formats (e.g., Apache Iceberg, Delta Lake), and engineering practices that improve client outcomes.

Contribute proof-of-concepts, reusable data accelerators, engineering assets, and innovation initiatives.

Support enterprise adoption of Artificial Intelligence, MLOps, and Retrieval-Augmented Generation (RAG) capabilities by building the foundational data layers required to support them.

Consulting & Collaboration

Partner directly with client stakeholders to understand business challenges and translate requirements into practical technical solutions.

Collaborate with CGI architects, engineers, and consultants to deliver successful client outcomes.

Participate in architecture reviews, technical workshops, design sessions, and strategic planning activities.

Support proposals, technical estimates, proof-of-concepts, and solution development when required.

Mentor junior data engineers and contribute to knowledge sharing across CGI's Data Platform Engineering practice.

Required qualifications to be successful in this role:

Engineering Experience

Demonstrated experience designing, implementing, and supporting enterprise data platforms, data lakes, or data warehouses within complex business environments.

Experience delivering Data Engineering, DataOps, Big Data, or Analytics solutions.

Experience contributing to data modernization, cloud migration, or enterprise BI/AI initiatives.

Strong understanding of modern data architecture practices and the ability to apply them across diverse client environments.

Ability to quickly learn and apply new data technologies and engineering approaches.

Data Platform Engineering

Experience with concepts such as:

Data Lakes, Data Warehouses, and Lakehouse architectures

ETL / ELT pipeline design and orchestration

DataOps & CI/CD for Data

Data Governance, Cataloging, and Lineage

Distributed Data Processing

Open Table Formats (Delta Lake, Iceberg)

Core Data Technologies

Deep expertise in one or more enterprise data platforms and tools, including:

Databricks / Apache Spark

Snowflake

Apache Airflow, dbt (data build tool)

Event Streaming (Apache Kafka, Confluent, Azure Event Hubs)

Cloud Engineering (Adjacent Skills)

Experience working with enterprise cloud platforms and deploying infrastructure via code, including:

Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP)

Infrastructure as Code (Terraform, Bicep, ARM)

Containers & Kubernetes (understanding how to run data workloads in containers)

Programming & Automation

Strong proficiency in languages used for data processing and automation:

Python

SQL (Advanced tuning and analytics)

Scala or Java (Bonus)

Bash / PowerShell

Professional Skills

Strong analytical and problem-solving skills.

Excellent written and verbal communication skills.

Ability to communicate effectively with both technical and business stakeholders.

Strong collaboration and relationship-building skills.

Adaptability and a willingness to learn new technologies.

Passion for engineering excellence and continuous improvement.

Experience mentoring or supporting other engineers.

Experience That Will Help You Succeed

The following experience is considered an asset but is not required:

Artificial Intelligence & MLOps

Experience building infrastructure to support AI Platforms, Generative AI, or MLOps.

Understanding vector databases and data prep for Retrieval-Augmented Generation (RAG).

General Platform Engineering

Experience with developer self-service, Internal Developer Platforms (IDPs), or traditional CI/CD pipelines (GitHub Actions, GitLab CI/CD).

Cloud Financial Management

Experience with FinOps, specifically optimizing compute costs for massive data platforms like Snowflake or Databricks.

Industry Experience

Experience supporting clients within Financial Services, Government, Healthcare, Telecommunications, Insurance, Utilities, or other regulated industries.

Types of Client Engagements

As a member of CGI's Data Platform Engineering practice, you may contribute to initiatives such as:

Data Modernization & Cloud Migration

Enterprise Lakehouse Implementation

DataOps Transformation & Automation

Artificial Intelligence / MLOps Enablement

Real-time Analytics & Streaming Data Platforms

Enterprise Data Governance Initiatives

Technologies We Commonly Work With

Depending on the client engagement, you may work with technologies such as:

Data & Analytics Platforms

Databricks (Delta Lake, Unity Catalog)

Snowflake

Microsoft Fabric

AWS Glue / Athena / Redshift

Google BigQuery

Orchestration & Transformation

Apache Airflow

dbt (data build tool)

Apache Kafka

Cloud Platforms & Infrastructure

AWS, Microsoft Azure, Google Cloud Platform

Terraform, Ansible

Kubernetes, Docker

Programming & Query Languages

Python, SQL, Scala

What Success Looks Like

Successful Senior Data Platform Engineers at CGI:

Deliver secure, scalable, and reliable data architectures that create measurable client value and enable analytics/AI.

Build trusted relationships with clients through technical expertise and professionalism.

Adapt quickly to new data ecosystems, industries, and client environments.

Contribute reusable data engineering assets, automation, and best practices to the Data Platform practice.

Mentor teammates and actively share knowledge across the organization.

Continuously improve data operations processes and delivery practices.

Demonstrate ownership, accountability, collaboration, and commitment to continuous learning.

CGI is providing a reasonable estimate of the pay range for this role. The determination of this range includes factors such as skill set level, geographic market, experience and training, and licenses and certifications. Compensation decisions depend on the facts and circumstances of each case. A reasonable estimate of the current range is $95,000-$145,000. This role is a future opening.

#LI-AB19

Use of the term 'engineering' in this job posting refers to the technical sense related to Information Technology (IT) and does not imply that the individual practices engineering or possesses the requisite license as prescribed by the applicable provincial or territorial engineering regulator. We are seeking individuals with expertise in IT engineering-related functions, but licensure from an engineering regulator is not a prerequisite for this position. Engineering is a regulated profession in Canada which is restricted in terms of use of titles and designation.

Skills:

• Finance&Ops Apps Solution Arch