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Posted 6 days ago

Senior AWS Data Engineer (Strong Databricks)

@ Ccube

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Mississauga, Ontario

Full-time
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SIGNALS

Requirements summary:

  • Experience: 8+ Years
  • Must-Have Skills
  • 8–10+ years of experience in Data Engineering, with strong hands-on experience building production-grade data pipelines.
  • Strong hands-on experience with AWS and/or Azure data services.
  • Strong knowledge of Data Lakes, Lakehouses, Data Warehousing, and Medallion Architecture.
  • Hands-on experience with Apache Spark and/or Databricks.
Source and dates

Source type not verified · apify-linkedin

Employer posting date
2026-11-04
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.

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Job description
We are looking for a Senior AWS Data Engineer to design and lead scalable, secure, and high-performance cloud data platforms across AWS/Azure." in Mississauga, ON (Hybrid).
 Senior AWS Data Engineer (Strong Databricks)
Location: Mississauga, ON (Hybrid)
Full Time/CTH

Experience: 8+ Years

Role Expectations
The Senior Data Engineer will be responsible for hands-on development, implementation, optimization, and maintenance of enterprise-scale data solutions. The role requires strong technical ownership of data pipelines, data processing frameworks, and cloud data platforms, focusing on performance, reliability, data quality, and cost efficiency.
Key Responsibilities
  • Design, develop, and maintain scalable cloud-based data pipelines and data platforms using AWS and/or Azure.
  • Build and optimize data lakes, data warehouses, and lakehouse solutions.
  • Implement Medallion Architecture (Bronze, Silver, and Gold layers) for efficient data processing and data quality.
  • Develop and maintain robust ETL/ELT pipelines for batch and real-time data processing.
  • Build data integration workflows using SQL, Python, Spark, Databricks, Kafka, Airflow, dbt, or equivalent technologies.
  • Develop and optimize complex SQL queries, data models, and transformation logic.
  • Work with structured, semi-structured, and unstructured data from multiple sources.
  • Implement data quality checks, validation frameworks, error handling, and monitoring mechanisms.
  • Apply data governance, security, access control, and compliance best practices.
  • Optimize data pipeline performance, scalability, reliability, and cloud infrastructure costs.
  • Implement CI/CD pipelines, automated testing, and DevOps practices for data engineering solutions.
  • Collaborate with Data Architects, Analytics Engineers, Data Scientists, and business stakeholders to deliver reliable data solutions.
  • Contribute to technical design documents, data flow diagrams, and implementation plans.
  • Troubleshoot production issues and ensure data pipeline availability, accuracy, and timely delivery.
  • Mentor junior Data Engineers and promote engineering best practices.
Must-Have Skills
  • 8–10+ years of experience in Data Engineering, with strong hands-on experience building production-grade data pipelines.
  • Strong hands-on experience with AWS and/or Azure data services.
  • Expertise in ETL/ELT development, data transformation, and data pipeline orchestration.
  • Strong knowledge of Data Lakes, Lakehouses, Data Warehousing, and Medallion Architecture.
  • Advanced SQL skills and proficiency in Python or equivalent programming languages.
  • Hands-on experience with Apache Spark and/or Databricks.
  • Experience with Kafka, Airflow, dbt, or equivalent streaming and orchestration technologies.
  • Experience processing structured, semi-structured, and unstructured data.
  • Strong understanding of data modeling, data quality, and data validation techniques.
  • Knowledge of data governance, security, access management, and compliance practices.
  • Experience with Git, CI/CD, automated testing, and DevOps practices.
  • Strong analytical, problem-solving, communication, and collaboration skills.
Good to Have
  • AWS or Azure certifications.
  • Databricks certification or hands-on experience with the Databricks platform.
  • Experience with Snowflake or other modern cloud data warehouses.
  • Experience with real-time data processing and streaming architectures.
  • Familiarity with Infrastructure as Code (Terraform, CloudFormation, or Bicep).
  • Knowledge of data observability, monitoring, and data lineage tools.
  • Exposure to Data Mesh concepts and distributed data architectures

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