Data Engineer
ShyftLabs
full-time
Posted on:
Location Type: Remote
Location: Canada
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Tech Stack
About the role
- Design, build, and maintain scalable and reliable batch and real-time ETL/ELT data pipelines using cloud services such as GCP Dataflow, Cloud Functions, Pub/Sub, and Cloud Composer.
- Architect and implement robust data infrastructure capable of handling high-volume data ingestion and processing.
- Develop and manage our central data warehouse in Google BigQuery.
- Design and implement data models, schemas, and table structures optimized for performance, scalability, and long-term maintainability.
- Write clean, efficient, and maintainable SQL and Python code to transform raw data into curated, analysis-ready datasets.
- Build reliable transformation workflows that support analytics, reporting, and data science initiatives.
- Monitor, troubleshoot, and optimize data infrastructure to ensure high performance, reliability, and cost efficiency.
- Implement BigQuery best practices, including partitioning, clustering, query optimization, and materialized views.
- Build and maintain curated data models that serve as the “source of truth” for business intelligence and reporting.
- Ensure data is optimized and readily accessible for BI tools such as Looker and other analytics platforms.
- Implement automated data quality checks, validation rules, and monitoring frameworks to ensure the integrity and reliability of data pipelines and warehouse systems.
- Establish processes for data governance, observability, and lineage tracking.
- Work closely with software engineers, data analysts, and data scientists to understand their data requirements and provide the necessary infrastructure and data products.
- Lead and support client and stakeholder communication, working with enterprise clients to translate business needs into scalable data solutions.
- Partner with product teams and leadership to ensure that technical data solutions align with business strategy and client expectations.
- Take ownership of data platforms and architecture decisions, helping shape the future direction of our analytics and data infrastructure.
- Identify opportunities to improve data reliability, automate workflows, and generate new insights through data.
- Contribute to a collaborative, high-performing engineering culture with strong communication and teamwork.
Requirements
- 5+ years of hands-on experience in data engineering, data integration, or data platform development.
- Degree in Computer Science, Engineering, Mathematics, or related STEM discipline.
- Strong programming and query skills in SQL and Python.
- Experience working with distributed version control systems such as Git in an Agile/Scrum environment.
- Experience designing and orchestrating ETL pipelines, particularly with Databricks.
- Experience working within cloud environments (GCP, AWS, or Azure).
- Experience with database systems such as MongoDB and Elasticsearch.
- Strong understanding of data warehousing and dimensional modeling methodologies.
- Hands-on experience with Airflow and Hadoop.
- Experience using Docker for containerized workflows and reproducible environments.
- Ability to identify opportunities to improve data quality, reliability, and automation.
- Strong business awareness and communication skills, with the ability to collaborate with both technical teams and business stakeholders.
- Experience within the retail industry is a plus.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
ETLELTdata pipelinesSQLPythondata warehousingdimensional modelingdata governancedata qualitydata integration
Soft Skills
communicationcollaborationleadershipteamworkbusiness awareness