Senior Software Engineer, Data
New
Canada, EST/EDTFull-TimeSenior
Salary$180,000 to $230,000 CAD
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Job Details
- Experience
- 5 to 8 years
- Required Skills
- PythonSQLGCPAirflowData engineeringData modelingBigQuery
Requirements
- 5 to 8 years of professional experience building and operating production-grade data systems.
- Strong SQL expertise and proficiency in Python.
- Experience designing and maintaining APIs, data services, and scalable backend data solutions.
- Strong understanding of modern cloud data platforms, preferably Google Cloud Platform, or equivalent experience with AWS or Azure.
- Hands-on experience with cloud data warehouses such as BigQuery, ELT workflows, and orchestration tools such as Airflow.
- Deep knowledge of data modeling concepts, including dimensional modeling, slowly changing dimensions, and incremental processing strategies.
- Strong focus on data quality, governance, observability, reliability, and documentation.
- Experience working cross-functionally with technical and non-technical stakeholders.
- Ability to communicate clearly, make sound technical decisions, and take ownership in a high-autonomy environment.
- Experience using AI-powered development tools for coding, debugging, planning, and code reviews.
- Startup experience, embedded analytics development, multi-tenant data products, or AI-ready data architecture experience are considered strong assets.
Responsibilities
- Design, build, and maintain scalable data pipelines capable of processing large volumes of transactional data using modern data engineering technologies.
- Develop and own end-to-end data models, transformations, and analytics solutions that support business intelligence, operational reporting, and machine learning capabilities.
- Build and improve embedded analytics infrastructure and data products that directly support customer-facing experiences.
- Evolve cloud-based data platforms, including data warehouses, databases, and data ingestion systems, while ensuring scalability and reliability.
- Establish and improve data quality practices through automated testing, monitoring, observability, validation frameworks, and reliability improvements.
- Own data lineage, metadata management, documentation, and semantic clarity to prepare data systems for AI-powered and LLM-based use cases.
- Make architectural decisions, contribute to technical strategy, and create design documentation that supports long-term platform growth.
- Collaborate with product, machine learning, and business teams to translate requirements into effective data solutions.
- Use AI-assisted and agentic development tools to improve engineering productivity, accelerate problem-solving, and enhance delivery quality.
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