Forward Deployed Data Architect
New
S
ShyftLabsData technology
Listing location: Toronto, Ontario; Workplace type: RemoteFull-TimeSenior
Salary160,000 - 190,000 CAD per year
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Job Details
- Experience
- 8+ years of overall experience in data engineering, data architecture, or related technical roles
- Required Skills
- PythonSQLApache AirflowPySpark
Requirements
- Have 8+ years of experience in data engineering, data architecture, or related technical roles, including hands-on delivery of production-grade data platforms.
- Bring experience in a client-facing technical role working directly with enterprise customers or stakeholders.
- Demonstrate hands-on expertise designing and implementing enterprise solutions with Azure Databricks.
- Have a deep understanding of Delta Lake, including data management, processing patterns, and performance optimization.
- Have experience with Unity Catalog, data governance, access controls, lineage, and enterprise data management.
- Be proficient in SQL and Python/PySpark for data processing, transformation, and pipeline development.
- Have hands-on experience with Apache Airflow orchestration and dbt Core transformation, testing, and modeling.
- Have experience designing ingestion pipelines across APIs, relational databases, files, and other enterprise data sources.
- Understand Medallion Architecture, data products, semantic modeling, and data quality frameworks.
- Have experience with CI/CD, Git, automated testing, monitoring, and production data pipeline management.
- Be able to review and challenge architectures, code, data models, and engineering deliverables and provide actionable feedback.
- Be able to translate business requirements into technical specifications, architecture designs, and implementation roadmaps.
- Have experience working with external technology partners, third-party delivery teams, or distributed engineering organizations.
Responsibilities
- Lead client-facing architecture discussions to understand business objectives, technical requirements, data environments, and architectural challenges.
- Design and recommend enterprise data platform architectures using Azure Databricks, Delta Lake, and cloud-based data engineering technologies.
- Facilitate technical workshops, discovery sessions, and architecture reviews to define target-state architectures.
- Develop, validate, and review data engineering solutions using SQL, Python/PySpark, Apache Airflow, and dbt Core.
- Establish standards for ingestion pipelines connecting APIs, databases, files, and cloud data sources, including Databricks Lakeflow Connect and Auto Loader.
- Design and guide Bronze, Silver, and Gold data layers and establish practices for Unity Catalog, access controls, lineage, semantic modeling, and governance.
- Review partner and engineering deliverables, including architectures, code, data models, and pipelines, for requirements, performance, security, and maintainability.
- Define data validation, automated testing, monitoring, error-handling, and operational-readiness standards.
- Guide Git-based workflows, automated testing, CI/CD pipelines, and controlled deployments across development, testing, and production.
- Coordinate client stakeholders, internal engineers, and external technology partners, communicating decisions, trade-offs, dependencies, risks, and recommendations.
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