Data Engineer

This is a fully remote position, open to applicants in Canada.

📋 Description

• Design, implement, and sustain batch, incremental, and file-based data pipelines to facilitate analytics, reporting, and operational use cases.

• Assist with data migration projects, encompassing the transition of data from legacy systems to modern platforms, ensuring continuity, accuracy, and reconciliation.

• Collaborate with enterprise data integration tools like Talend, including maintaining current Talend jobs and aiding in the migration or re-implementation of Talend pipelines to other platforms when necessary.

• Create and uphold a solid data engineering environment (e.g., separation of dev/test/prod, access controls, naming conventions, source control, deployment processes, and monitoring) that supports reliable delivery and secure change management.

• Facilitate ingestion, storage, and management of unstructured and semi-structured datasets (e.g., documents, PDFs, text extracts, files, metadata) alongside traditional relational data.

• Enhance and optimize data models within Power BI dashboards and AI-driven analytics.

• Prepare and organize analytics- and AI-ready datasets, including clean, well-defined tables and reference data utilized in automation and AI-driven solutions.

• Work closely with AI & Automation Specialists to ensure data requirements for AI projects are thoroughly understood, accurately sourced, validated, and production-ready.

• Convert business and reporting requirements into scalable data models, transformations, and pipeline designs.

• Create and sustain trusted datasets that underpin dashboards, scorecards, client reporting, and downstream analytics.

• Implement data quality checks, validation logic, reconciliation procedures, and monitoring to guarantee data reliability and consistency.

• Diagnose and resolve data issues across ingestion, transformation, and consumption layers, identifying root causes and implementing remediation actions.

• Develop and maintain comprehensive documentation, including source-to-target mappings, data definitions, data lineage, and known limitations.

• Collaborate with analytics, IT, data governance, and security stakeholders to ensure data solutions adhere to privacy, security, and regulatory standards.

• Contribute to the prioritization, planning, and execution of multiple data engineering, migration, and enablement initiatives concurrently.

⛳️ Requirements

• Bachelor’s Degree in Computer Science, Information Technology, Data Analytics, or a related field.

• Over 4 years of experience in data engineering, analytics engineering, business intelligence, or data platform roles.

• More than 4 years of experience with data integration and ETL/ELT tools (e.g., Talend, Azure Data Factory, or comparable enterprise platforms).

• Familiarity with Microsoft Power Automate (or equivalent workflow automation tools) to manage process automation, notifications, and data movement/integration.

• Proficiency in Python (or similar scripting languages) for data transformations, automation, API integrations, and lightweight tooling.

• Knowledge of data orchestration and scheduling patterns, including dependency management, retries, and operational runbooks for production pipelines.

• Experience in implementing data quality and observability practices (e.g., validation rules, monitoring/alerting, SLAs) to ensure reliable and trustworthy datasets.

• Working understanding of secure data handling and privacy-by-design principles (e.g., least-privilege access, encryption, PHI/PII considerations) in the development and operation of data pipelines.

• Strong SQL skills for data transformation, validation, reconciliation, and performance optimization.

• Experience with Power BI (data modeling, DAX fundamentals, and performance considerations) to support scalable dashboards and self-service analytics.

• Experience working with both structured and unstructured datasets, including file-based or document-oriented data sources.

• Familiarity with relational and cloud-based data platforms utilized for analytics and reporting.

• Experience in supporting data migrations, legacy system consolidation, or platform modernization projects.

• Understanding of data quality, documentation practices, and foundational data governance principles.

• Strong analytical and problem-solving capabilities, particularly in identifying data quality issues and pipeline failures.

• Ability to collaborate effectively with both technical and non-technical stakeholders, including analytics and AI delivery teams.

• Excellent written and verbal communication skills, with the ability to clearly document data flows, dependencies, and assumptions.

🏝️ Benefits

• Health insurance

• 401(k) matching

• Flexible work hours

• Paid time off

• Remote work options