Data Engineer

ODAIA

Data Engineer building scalable pipelines and AI-powered automation for ODAIA’s life sciences SaaS platform. Integrating healthcare data through AWS, Snowflake, Python, dbt, Veeva, and Salesforce.

Posted 8/6/2026full-timeRemote • 🇨🇦 CanadaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building scalable data infrastructure and implementing end-to-end data solutions using AWS tools. Proficient in data integration, architecture, and management best practices, with strong programming skills in Python and experience in optimizing data models.

Highest-signal resume keywords

AWS Tools And ServicesData EngineeringPython ProgrammingETL ProcessesSQL Schema Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data InfrastructureData IntegrationData SolutionsData ModelsAutomation ToolsProduction Incident ManagementData PipelinesOrchestration ToolsDatabase DesignData Structures

Soft Skills

Project ManagementCommunication SkillsAdaptabilityPrioritization

Tools & Technologies

AWS ECSAWS S3AWS LambdaAWS DynamoDBAPI GatewaySnowflakeDagsterAirflowDbtCI/CD

Industry Keywords

Healthcare DatasetsPharmaceutical DatasetsPharmacy DatasetsCloud-Native ArchitecturesCRM Integration

Tech Stack

Tools & technologies

AirflowAWSCloudDynamoDBETLPythonSQL

About the role

Key responsibilities & impact

Build scalable data infrastructure supporting customer onboarding and implementation
Design and ship Claude-powered tools to automate production incident error triage and fix proposals
Develop automation tools for configuring and ingesting new customer datasets
Define, prototype, and implement end-to-end data solutions for complex data challenges
Advise internal teams and customers on data integration, architecture, and management best practices
Implement scalable ingestion pipelines using AWS tools and services
Develop integrations with Veeva, Salesforce, and other third-party data sources
Contribute to proof-of-concept builds and internal applications
Design APIs and data models for internal data access and cross-system interoperability
Explore and onboard new data sources
Design warehouse schemas to improve analytics and product capabilities
Collaborate with product and engineering teams to align data systems with strategic objectives

Requirements

What you’ll need

3+ years of experience in data engineering and production-grade data pipelines
Strong experience with AWS, including ECS, S3, Lambda, DynamoDB, and API Gateway
Experience with Snowflake
Experience with Dagster or Airflow for orchestration and workflow management
Strong programming proficiency in Python
Experience building and optimizing complex data models with dbt
Strong understanding of ETL processes
Solid understanding of data structures, algorithms, and database design
Expertise in performant SQL schema and query development
Familiarity with CI/CD and test-driven development in a production environment
Experience integrating CRM systems such as Veeva and Salesforce
Experience handling healthcare, pharmaceutical, or pharmacy-related datasets is a strong asset
Demonstrated experience with the AWS stack or an equivalent cloud platform
Background modernizing data environments and migrating legacy systems to cloud-native architectures
Ability to manage projects independently, prioritize effectively, and adapt to changing business needs
Excellent communication skills for translating complex technical concepts to technical and non-technical stakeholders

Benefits

Comp & perks

Meaningful stock option grants
Immediate medical/dental enrollment
Flexible time off
Work-from-home flexibility
Intentional, high-value in-person collaboration and socials
Career development opportunities
Values-based culture
AI-native environment using AI and agentic automation