Cloud Data Engineer | Toronto, ON | Fulltime FTE

Acestack; Toronto; Hybrid; CAD 90,000 - 140,000; Full time

Job summary

Acestack is seeking a Cloud Data Engineer to design, develop, and maintain scalable cloud-based data platforms and pipelines. You will work across AWS, Azure, and GCP, building ETL/ELT processes, data lakes, warehouses, and data marts for analytics and reporting.

Ideal candidates have hands-on experience with batch and streaming pipelines, data governance, and CI/CD practices. Hybrid work in Toronto, with a focus on performance, cost efficiency, and secure data management.

Qualifications

• Experience across AWS, Azure, or GCP data services.
• Strong ETL/ELT development and data integration expertise.
• Hands-on with batch and real-time data pipelines.
• Experience designing data lakes, warehouses, and marts.

Responsibilities

• Design, develop, and maintain scalable batch and real-time data pipelines.
• Build cloud-based data platforms using AWS, Azure, or GCP.
• Develop ETL/ELT processes to integrate multiple data sources.
• Design data lakes, warehouses, and data marts for analytics.
• Implement data integration with modern big data technologies.
• Ensure data quality, security, and governance across platforms.
• Monitor and optimize pipelines for reliability and performance.
• Troubleshoot production data issues and outages.
• Collaborate with analysts, data scientists, and stakeholders on data requirements.
• Optimize storage, processing, and compute for cost efficiency.
• Implement CI/CD, IaC, and automation for data platforms.
• Maintain metadata, lineage, and data catalogs.

Skills

Cloud Data Engineering

ETL/ELT development

Data lakes, data warehouses

Batch and streaming pipelines

CI/CD and IaC

SQL and Python/Java/Scala

Tools

Apache Airflow

Terraform

CloudFormation

Snowflake/Databricks/BigQuery

Azure/AWS/GCP data services

Job description

Cloud Data Engineer

Location: Toronto, ON
Work Model: Hybrid
Employment Type: Full-Time Permanent (FTE)

Job Summary

We are seeking an experienced Cloud Data Engineer to design, develop, and maintain scalable cloud-based data platforms and pipelines. The ideal candidate will have strong experience in cloud data engineering, ETL/ELT, data lakes, data warehouses, real-time and batch processing, and cloud-native data services across AWS, Azure, or Google Cloud.

Key Responsibilities

• Design, develop, and maintain scalable batch and real-time data pipelines.

• Build, implement, and manage cloud-based data platforms using AWS, Microsoft Azure, or Google Cloud Platform (GCP).

• Develop robust ETL/ELT processes to extract, transform, and load data from multiple structured and unstructured sources.

• Design and optimize data lakes, data warehouses, and data marts.

• Implement cloud-native data integration solutions using modern data engineering and big data technologies.

• Ensure data quality, integrity, security, privacy, and compliance across enterprise data platforms.

• Monitor, troubleshoot, and optimize data pipelines to ensure reliability and performance.

• Investigate and resolve production data issues and pipeline failures.

• Collaborate with Business Analysts, Data Scientists, Data Architects, Application Developers, and business stakeholders to understand and implement data requirements.

• Optimize data storage, processing, and compute resources for performance and cost efficiency.

• Implement CI/CD, automation, and Infrastructure as Code (IaC) practices for cloud data platforms.

• Manage metadata, data lineage, data cataloging, and data governance processes.

• Develop solutions that support reporting, analytics, AI/ML, and Business Intelligence initiatives.

• Contribute to cloud data architecture, modernization, and continuous improvement initiatives.

Required Skills & Experience

• Strong hands-on experience in Cloud Data Engineering.

• Experience building and managing data solutions on one or more major cloud platforms:

  • AWS

  • Microsoft Azure

  • Google Cloud Platform (GCP)

• Strong experience with ETL/ELT development and data integration.

• Experience designing and implementing data lakes, data warehouses, and data marts.

• Hands-on experience developing batch and real-time/streaming data pipelines.

• Strong knowledge of data modeling, data processing, and data engineering best practices.

• Experience with cloud-native data services and big data technologies.

• Experience with data quality, data validation, security, and governance.

• Strong troubleshooting and performance optimization skills.

• Experience with CI/CD and Infrastructure as Code (IaC).

• Proficiency in SQL and experience with at least one programming language such as Python, Java, or Scala.

Preferred Qualifications

• Experience with distributed data processing technologies such as Apache Spark, Kafka, or similar platforms.

• Experience with cloud data warehouse technologies such as Snowflake, Databricks, Amazon Redshift, Azure Synapse, or BigQuery.

• Experience with data orchestration tools such as Apache Airflow, Azure Data Factory, AWS Glue, or similar technologies.

• Experience with Terraform, CloudFormation, or ARM/Bicep for Infrastructure as Code.

• Knowledge of data cataloging, metadata management, data lineage, and enterprise data governance.

• Experience supporting AI/ML and advanced analytics use cases.

• Relevant AWS, Azure, or Google Cloud certifications are an asset.