Designs, develops, and leads enterprise-scale AWS and Azure data platforms, pipelines, lakes, warehouses, and lakehouses. Builds ETL/ELT workflows using SQL, Python, Spark, Databricks, Kafka, Airflow, and dbt; implements data quality, governance, security, monitoring, CI/CD, and automated testing. Optimizes performance, reliability, scalability, and cloud costs, troubleshoots production issues, collaborates with technical and business stakeholders, and mentors junior engineers.
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
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.
- 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.
- 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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