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AWS Data Platform Engineering Lead – AWS Data Services

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Summary

Leads the design and build of scalable, cloud-native data platforms on AWS, owning batch and streaming pipelines, engineering standards, data quality, and cost optimization while mentoring mid-level data engineers. Core stack: AWS data services (S3, Glue, Redshift, EMR, Kinesis, MSK), Python/Scala/Java, Airflow, and Terraform/IaC. Hybrid role in Toronto (2 days/week in office).

AWS Data Platform Engineering Lead – AWS Data Services

Location: Toronto: Hybrid 2 days a week in office

Role Overview

We are seeking a Senior Data Engineer with strong AWS expertise to lead the design, development, and evolution of scalable, high-performance data platforms.

This role is responsible for building cloud-native data engineering frameworks, optimizing complex data pipelines, and evaluating emerging AWS services to drive innovation and efficiency.

As a senior technical leader, you will collaborate with architecture, analytics, and business teams to define long-term data strategy, establish engineering best practices, and support enterprise-wide data initiatives.

Key Responsibilities

  • Lead the design, development, and enhancement of cloud-native data engineering frameworks on AWS.
  • Architect, build, and optimize end-to-end batch and streaming data pipelines for scalability, reliability, and performance.
  • Drive experimentation and adoption of new and emerging AWS services to improve platform capabilities and operational efficiency.
  • Establish data engineering standards, patterns, and best practices for data ingestion, transformation, and storage.
  • Partner with analytics and product teams to support reporting, analytics, and business objectives.
  • Own data quality, monitoring, observability, and cost optimization strategies across the data platform.
  • Provide technical leadership and mentorship to mid-level data engineers.
  • Participate in architectural reviews and contribute to enterprise-wide data strategy and decision-making.

Required Qualifications

  • 10+ years of experience in Data Engineering or Software Engineering.
  • 4-5 years of dedicated hands-on experience with AWS.
  • Proven experience designing and implementing large-scale, production-grade data platforms.
  • Expertise with AWS data services, including:
    • Amazon S3
    • AWS Glue
    • Amazon Redshift
    • Amazon EMR
    • AWS Lambda
    • AWS Step Functions
    • Amazon Kinesis
    • Amazon MSK
  • Strong programming skills in Python, Scala, or Java.
  • Deep experience with ETL/ELT frameworks and data pipeline orchestration tools, including Airflow and AWS-native orchestration services.
  • Strong understanding of data modeling, distributed systems, and performance tuning.
  • Experience with CI/CD practices and Infrastructure as Code tools such as Terraform, AWS CDK, or CloudFormation.
  • Excellent communication skills with the ability to explain technical concepts to non-technical stakeholders.

Preferred Qualifications

  • Experience evaluating and implementing new AWS services through proof-of-concept initiatives.
  • Exposure to real-time streaming data architectures.
  • Knowledge of data governance, security, and compliance within cloud environments.
  • Experience supporting analytics, business intelligence, or machine learning platforms.
  • Experience working within Agile frameworks.
  • AWS certifications such as:
    • AWS Certified Data Analytics – Specialty
    • AWS Certified Solutions Architect

Technical Leadership Expectations

  • Serve as a senior technical leader for AWS data engineering initiatives.
  • Influence data platform architecture, engineering standards, and best practices.
  • Guide teams in building scalable, reliable, and cost-effective data solutions.
  • Promote innovation through the evaluation and adoption of emerging AWS capabilities.

Skills

See also

Data Engineering jobs by country — openings, pay and top skills →

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