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Capgemini

Senior Data Engineer

Calgary, AB, Canada

The base compensation range for this role in the posted location is $83,951 to $135,000

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Job details

Pay: The base compensation range for this role in the posted location is $83,951 to $135,000
Apply by: Dec 15, 2026
Posting language: English
Posted: Oct 30, 2026

Original job description

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

Description and Location Strong/expert Spark (PySpark) Using Jupyter Notebooks, Colab or DataBricks (preferred) Hands-on data pipeline development, ingest patterns in Azure Orchestration tools, ADF or Airflow SQL Denormalized Data modeling for big data systems Responsibilities and essential job functions include but are not limited to the following: Demonstrate deep knowledge of the data engineering domain to build and support non-interactive (batch, distributed) & real-time, highly available data, data pipeline, and technology capabilities Build fault-tolerant, self-healing, adaptive, and highly accurate data computational pipelines Provide consultation and lead the implementation of complex programs Develop and maintain documentation relating to all assigned systems and projects Tune queries running over billions of rows of data running in a distributed query engine Perform root cause analysis to identify permanent resolutions to software or business process issues.

We are seeking an experienced Databricks Data Engineer to design, develop, and maintain scalable data pipelines and cloud-based data platforms. The ideal candidate will have strong expertise in Databricks, PySpark, Python, SQL, Delta Lake, and cloud technologies (Azure/AWS)*, with experience building enterprise-scale data solutions that support analytics, reporting, and machine learning workloads. Key Responsibilities - Design, develop, and optimize scalable ETL/ELT pipelines using Databricks and PySpark. - Build and maintain data ingestion frameworks for batch and real-time data processing. - Develop data models and implement data transformation logic using SQL and Python. - Leverage Delta Lake for data management, data quality, and performance optimization. - Implement data orchestration workflows using tools such as Azure Data Factory, Airflow, or Databricks Workflows. - Collaborate with data architects, analysts, data scientists, and business stakeholders to understand requirements and deliver solutions. - Optimize Databricks jobs and Spark applications for performance, scalability, and cost efficiency. - Implement data governance, security, and compliance best practices. - Monitor and troubleshoot production data pipelines and provide support for critical issues. - Participate in code reviews, documentation, and knowledge-sharing activities. Technical Skills: Strong/expert Spark (PySpark) Using Jupyter Notebooks, Colab or DataBricks (preferred) Hands-on data pipeline development, ingest patterns in Azure Orchestration tools, ADF or Airflow SQL Denormalized Data modeling for big data systems

Job Description

Data engineers are responsible for building reliable and scalable data infrastructure that enables organizations to derive meaningful insights, make data-driven decisions, and unlock the value of their data assets.

Job Description - Grade Specific

The involves leading and managing a team of data engineers, overseeing data engineering projects, ensuring technical excellence, and fostering collaboration with stakeholders. They play a critical role in driving the success of data engineering initiatives and ensuring the delivery of reliable and high quality data solutions to support the organizations data driven objectives.

The base compensation range for this role in the posted location is $83,951 to $135,000

Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.

The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.

These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.

It is not typical for candidates to be hired at or near the top of the posted compensation range.

In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.

Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include:

Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave

Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
Life and disability insurance
Employee assistance programs
Other benefits as provided by local policy and eligibility

Important Notice: Compensation (including bonuses, commissions, or other forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini’s discretion, consistent with applicable laws. The Company reserves the right to amend or withdraw compensation programs at any time, within the limits of applicable legislation.

Disclaimers

Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. Capgemini also participates in the Partnership Accreditation in Indigenous Relations (PAIR) program which supports meaningful engagement with Indigenous communities across Canada by promoting fairness, accessibility, inclusion and respect. We value the rich cultural heritage and contributions of Indigenous Peoples and actively work to create a welcoming and respectful environment. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.

This is a general description of the Duties, Responsibilities and Qualifications required for this position. Physical, mental, sensory or environmental demands may be referenced in an attempt to communicate the manner in which this position traditionally is performed. Whenever necessary to provide individuals with disabilities an equal employment opportunity, Capgemini will consider reasonable accommodations that might involve varying job requirements and/or changing the way this job is performed, provided that such accommodation does not pose an undue hardship. Capgemini is committed to providing reasonable accommodation during our recruitment process. If you need assistance or accommodation, please reach out to your recruiting contact.

Please be aware that Capgemini may capture your image (video or screenshot) during the interview process and that image may be used for verification, including during the hiring and onboarding process.

Click the following link for more information on your rights as an Applicant in the United States. http://www.capgemini.com/resources/equal-employment-opportunity-is-the-law

Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.

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More about this job

Responsibilities

The role involves designing, developing, and optimizing scalable ETL/ELT pipelines while managing data infrastructure to support analytics and machine learning. You will lead a team of data engineers to ensure technical excellence and collaborate with stakeholders to deliver reliable data solutions.

Requirements

Candidates must have strong expertise in Spark, PySpark, Databricks, Python, and SQL for building enterprise-scale data solutions. Experience with cloud-based data platforms, data modeling, and orchestration tools like Azure Data Factory or Airflow is required.

Skills

Spark
PySpark
Databricks
Azure
Data pipeline development
SQL
Data modeling
Python
Delta Lake
Airflow
Azure Data Factory
ETL
ELT
Data governance
Cloud technologies
Distributed systems

Benefits

Paid time off
Company paid holidays
Personal days
Sick leave
Medical coverage
Dental coverage
Vision coverage
Retirement savings plans
Life insurance
Disability insurance
Employee assistance programs

Visa sponsorship

Not detected in the job text

Keywords

Spark
PySpark
Databricks
Azure
Data Engineering
ETL
ELT
SQL
Python
Delta Lake
Airflow
Azure Data Factory
Data Modeling
Cloud Computing
Distributed Query Engine
Data Governance
Machine Learning
Data Ingestion
Batch Processing
Real-time Processing
Jupyter Notebooks
Colab
Scalability
Data Infrastructure
Analytics
Reporting
Fault-tolerant
Data Quality
Performance Optimization
Consulting

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