Senior Data Engineer – Data Science (Amazon, Vancouver, BC, Canada)
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Department: Data Science
Location: Vancouver, British Columbia, Canada-Data Science Jobs US 2025
About the Team
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Amazon is where data becomes intelligence, and intelligence drives innovation. We’re looking for a Senior Data Engineer who is passionate about architecting scalable data solutions and mentoring engineering teams in best practices. This position sits at the critical intersection of data engineering and data science, helping Amazon teams unlock insights, build smarter systems, and serve customers better.
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As a Senior Data Engineer, you will be a technical lead on a team that designs, builds, and operates the data infrastructure that fuels leading-edge analytics and machine learning at scale. You will be a mentor to junior engineers, provide architectural guidance, and ensure the integrity, performance, and efficiency of our data pipelines and platforms.
Key Responsibilities
Create and implement scalable, high-performance, and reliable data pipelines to ingest, process, and store large volumes of data from numerous different sources in real-time and batch processing modes.
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Collaborate closely with software engineers, data scientists, and analysts to learn data needs and provide solutions that accommodate both experimental and production workflows.
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Lead technical efforts in data modeling, ETL pipeline design, workflow orchestration, data governance, and quality assurance.
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Assess and choose relevant technologies (e.g., AWS Redshift, EMR, Glue, S3, Lambda, Athena) based on project requirements.
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Mentor and support junior data engineers in best practices in data architecture, coding, and system design.
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Take responsibility for the complete life cycle of data infrastructure—from gathering requirements and prototyping to deployment and monitoring.
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Tune data systems for performance, cost, and scalability, especially in cloud.
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Push continual improvement by automating tedious processes and refining data delivery systems.
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Join code reviews, technical deep dives, and architecture discussions to guarantee top-notch deliverables.
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Be aware of emerging trends in data engineering and propose new tools or techniques as required.
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A Day in the Life
As a Senior Data Engineer_
.Your day as a Senior Data Engineer at Amazon in Vancouver may begin by checking the health of metrics of a mission-critical ETL job you launched last week. Following the debugging of a latency problem in a Redshift pipeline, you change gears and attend a sprint planning session with data scientists to learn about their new feature extraction requirements for a machine learning model. You refine their requests into horizontally scalable queries, propose a schema enhancement, and task junior team members with sections of the build while providing mentoring. Subsequently, you evaluate pull requests, write a new workflow in Confluence, and make a proposal for column-level data lineage implementation with AWS Glue Data Catalog.
Minimum Qualifications
Master’s degree (or experience) in Computer Science, Engineering, Information Technology, Analytics, Mathematics, or another quantitative field.
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5+ years of professional experience as a data engineer, software engineer, or similar roles.
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Solid coding skills in Python, Java, or Scala, especially in the context of constructing data processing solutions.
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SQL and relational database expertise, with a track record of optimizing complex queries.
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Extensive experience with designing and handling ETL/ELT pipelines with Apache Airflow, AWS Glue, or equivalents.
Experience
Knowledge of cloud-based data solutions (particularly AWS services like Redshift, S3, Lambda, EMR, Athena, Glue).
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Ability to mentor junior engineers, establish best practices, and drive architectural decision-making.
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Good grasp of data modeling principles (star schema, snowflake schema, normalization, denormalization).
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Experience in agile team work and utilization of source control, CI/CD, and issue tracking tools (e.g., Git, Jenkins, Jira).
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Excellent communication skills with the capability to convey complex technical concepts in simple business language.
Experience
Experience working with machine learning teams, shipping data features for training or inference pipelines.
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Knowledge of DevOps principles and infrastructure as code (e.g., CloudFormation, Terraform).
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Contributions to open-source projects or published work on data engineering or big data.
Data Science Jobs US 2025
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Preferred Qualifications
Experience with large-scale data infrastructure projects in a production setting.
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Previous experience with streaming technologies like Apache Kafka, Kinesis, or Flink.
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Knowledge of big data frameworks such as Apache Spark or Hadoop.
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Knowledge of best practices around data privacy, security, and governance, particularly in highly regulated environments.
Team and Culture
Innovation at Amazon begins with our people. We’re builders, problem solvers, and collaborators. Our data engineering teams run at enormous scale and drive some of the world’s most sophisticated analytical systems. From retail optimization through to cloud services, from supply chain innovation through to Alexa personalization, Amazon data engineers are making a difference globally on a daily basis.
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We’re committed to ownership, invention, and customer obsession. And we’re also passionately dedicated to ensuring our employees have the opportunity and resources to be successful. You’ll collaborate with top-notch engineers and scientists who are equally enthusiastic about creating robust systems as they are about mentoring, learning, and knowledge-sharing.
Compensation & Benefits
Amazon’s compensation varies with labor cost in various geographic areas. The base salary for the job is between $103,300/year and $172,600/year. Your actual compensation will depend on various factors such as your location, knowledge, skills, and experience related to the job.
Amazon is a total rewards company. As part of your base pay, you can also expect the following:
Equity (restricted stock units)
Sign-on bonuses
Annual performance-based bonuses
You’ll also be eligible for a comprehensive range of benefits, which may include:
Medical, dental, and vision insurance
Retirement savings plans
Paid time off and company holidays
Paid parental leave
Employee discounts and perks
Mental health and well-being support
Flexible working arrangements
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Experience providing technical leadership and mentoring other engineers for best practices on data engineering
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