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Capgemini

Data Engineer – PySpark & SQL

Mississauga, ON, Canada

Job type: Full-time
Apply by: Oct 31, 2026
Posting language: English
Posted: Oct 1, 2026

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Original job description

Job Title: Data Engineer – PySpark & SQL

Location: Mississauga, ON

Fulltime

We are looking for an experienced 3+ years of Data Engineer with strong hands-on expertise in PySpark, Python, SQL, and data engineering. The ideal candidate should have experience building scalable data pipelines and working with large datasets in cloud or distributed data environments.

Key Responsibilities

Design, develop, and maintain scalable data pipelines using PySpark and Python.
Develop complex and optimized SQL queries for data processing and transformation.
Build ETL/ELT workflows to ingest, transform, and process large datasets.
Perform data cleansing, validation, transformation, and performance optimization.
Work with structured and semi-structured datasets.
Troubleshoot data quality and pipeline performance issues.
Collaborate with business, analytics, and engineering teams to understand data requirements.
Follow data engineering best practices, coding standards, and documentation processes.

Required Skills

Strong hands-on experience with PySpark / Apache Spark.
Excellent proficiency in SQL, including complex queries, joins, CTEs, window functions, and query optimization.
Good programming experience with Python.
Experience developing ETL/ELT data pipelines.
Good understanding of data warehousing and data modeling concepts.
Experience working with large-scale/distributed data processing.
Exposure to at least one major cloud platform such as AWS, Azure, or GCP.
Strong problem-solving and debugging skills.

Certification Requirement

Candidate must hold at least one relevant, active Data Engineering or Cloud certification, preferably from:

Databricks Certified Generative AI Engineer Associate
Google Professional Machine Learning Engineer
NVIDIA Certified Professional - Agentic AI (NCP-AAI)
AWS Certified AI Practitioner

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More about this job

Responsibilities

Design, develop, and maintain scalable data pipelines and ETL/ELT workflows using PySpark, Python, and SQL to process large structured and semi-structured datasets. Optimize queries and pipeline performance, address data quality issues, and collaborate with business, analytics, and engineering teams to meet data requirements.

Requirements

Requires at least 3 years of data engineering experience, strong hands-on expertise in PySpark or Apache Spark, Python, SQL, and ETL/ELT pipeline development, plus knowledge of data warehousing, data modeling, and distributed processing. Candidates must hold at least one relevant active data engineering or cloud certification; experience with a major cloud platform and strong problem-solving skills are also required.

Skills

PySpark
Apache Spark
Python
SQL
ETL/ELT
Data Pipeline Development
Data Warehousing
Data Modeling
Distributed Data Processing
Cloud Platforms
Data Cleansing
Data Validation
Query Optimization
Performance Optimization
Troubleshooting
Data Quality

Visa sponsorship

Not detected in the job text

Keywords

Data Engineering
PySpark
Apache Spark
Python
SQL
ETL
ELT
Data Pipelines
Data Processing
Large Datasets
Distributed Computing
Cloud Platforms
AWS
Azure
GCP
Data Warehousing
Data Modeling
Structured Data
Semi-Structured Data
Data Cleansing
Data Validation
Query Optimization
Performance Optimization
Data Quality
Google Professional Machine Learning Engineer
NVIDIA Certified Professional - Agentic AI
AWS Certified AI Practitioner

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