Senior Data Engineer - Data Infra
New
J
JobgetherTechnology
Remote work opportunity within Canada.Full-TimeSenior
SalaryCA$133,600–CA$167,000
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLData engineeringBigQuerydbtLooker
Requirements
- 5+ years of professional experience in data engineering, data platforms, analytics engineering, or another SQL-intensive data role.
- Proven experience building and operating production-grade ingestion pipelines, connectors, and transformation workflows using dbt or equivalent technologies.
- Expert-level SQL skills.
- Strong working proficiency in Python or a comparable programming language for automation and platform tooling.
- Hands-on experience with Looker, Hex, dbt, or comparable BI and analytics technologies.
- Demonstrated ability to work cross-functionally, gather requirements, communicate technical concepts, and build consensus around data-platform decisions.
- Experience establishing new data engineering processes and solutions in environments characterized by ambiguity and rapid change.
- Strong troubleshooting, analytical, and problem-solving abilities.
- Strong communication and collaboration skills.
Responsibilities
- Design, build, operate, and scale reliable data ingestion pipelines and connectors using technologies such as Fivetran, dbt, and BigQuery.
- Develop and maintain data pipelines that bring information from diverse source systems into the central data warehouse.
- Own and evolve core data-platform components, including dbt models, semantic layers, orchestration, data quality, and observability.
- Build trusted, governed, and self-service data products that support business intelligence, analytics applications, and downstream teams.
- Partner closely with Finance, Go-to-Market, Manufacturing, ML, and other stakeholders to understand requirements and translate them into effective data solutions.
- Help establish data infrastructure that supports AI and agent-based workflows, including governed data access, LLM- and agent-compatible semantic layers, and appropriate scalability and safety controls.
- Troubleshoot data-platform issues hands-on, investigate root causes, and implement durable solutions.
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