Senior Analytics Engineer
- Toronto, Canada
- Senior Level
- 4+ years
- Posted
Skills
Required
- analytics
- modern cloud data warehouses
- Snowflake / Databricks / BigQuery / Redshift
- SQL
- complex queries
- dimensional data models
- data engineering
- business intelligence engineering
- Python
- data processing
- automation
- data governance
Join ZoomInfo as a Senior Analytics Engineer and play a crucial role in advancing the maturity of our data organization. You will design, build, and own the core infrastructure and data models that drive data-driven decision-making across the organization. Collaborate with cross-functional teams, establish best practices for analytics engineering, and communicate technical concepts clearly to both technical and non-technical audiences. Enjoy competitive compensation, equity awards, flexible paid time off, and a hybrid working model.
Design, develop, and maintain scalable analytics data models that support reporting, business intelligence, product analytics, and operational decision-making.
Build and manage trusted data pipelines and transformation workflows using modern analytics engineering practices.
Responsibilities
- Collaborate with data engineers and platform teams to optimize data architecture, warehouse performance, and transformation workflows.
Requirements
- Experience working with modern cloud data warehouses such as Snowflake, Databricks, BigQuery, or Redshift
- Strong SQL skills, including experience developing complex queries and dimensional data models
Strong communication, problem-solving, and organizational skills
- 4+ years of experience in analytics engineering, data engineering, business intelligence engineering, or a related field
- Experience building and maintaining analytics transformations using tools such as dbt or comparable frameworks
- Proficiency in Python or another programming language used for data processing and automation
- A thoughtful approach to data governance, security, privacy, and responsible use of data
Familiarity with data cataloging and lineage tools
- Strong understanding of data modeling concepts, including dimensional modeling, fact and dimension tables, slowly changing dimensions, and semantic layers
- Demonstrated ability to partner with cross-functional stakeholders and translate ambiguous business questions into scalable data solutions
- Experience implementing data quality testing, monitoring, documentation, and observability practices
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