Analytics Engineer

Role Overview

Tucows is seeking an experienced Analytics Engineer to join their dynamic team. The ideal candidate will have a strong background in data engineering, analytics, and machine learning, with the ability to drive data-driven decision-making across the organization.

What You Will Do

The Analytics Engineer will design, develop, and maintain complex data models in Snowflake, utilize dbt to create efficient data pipelines, and leverage Snowflake Intelligence features to implement conversational data queries and AI-driven insights.

Why It Might Be a Fit

The successful candidate will play a key role in shaping Tucows’ data culture and driving the adoption of cutting-edge data technologies and methodologies.

Requirements

• Bachelor’s degree in Computer Science, Statistics, or a related field; Master’s degree preferred
• 2+ years of experience in data analytics or a related field, with significant exposure to AI and Machine Learning applications in analytics
• Advanced SQL skills with experience in writing and optimizing complex queries on large-scale datasets
• dbt Proficiency: Hands-on experience with dbt (Data Build Tool) and its features for building, testing, and documenting data models
• Data Modeling: Expert-level knowledge of data modeling and data warehouse concepts (e.g., star schema, normalization, slowly changing dimensions)
• Snowflake & AI Capabilities: Experience with Snowflake’s Data Cloud platform and familiarity with its advanced AI capabilities (Snowflake Intelligence – Cortex Analyst, Cortex Agents, Cortex Search, AISQL, etc.) is highly preferred
• Business Intelligence Tools: Strong skills in Looker data visualization and LookML (including familiarity with Looker’s conversational AI and data agent capabilities) or similar BI tools
• AI Agents & Automation: Experience with AI agents or generative AI tools to optimize workflows and service delivery (such as creating chatbots or automated analytic assistants) is a plus
• Real-Time & Streaming Data: Experience with real-time data processing and streaming technologies (e.g., Kafka, Kinesis, Spark Streaming) for handling continuous data flows
• Programming: Proficient in Python for data analysis and manipulation (pandas, NumPy, etc.), with the ability to write clean, efficient code
• ETL/Orchestration: Familiarity with ETL processes and workflow orchestration tools like Apache Airflow (or similar scheduling tools) for automating data pipelines alongside Docker for local development and testing
• Cloud Platforms: Experience with cloud platforms and services (especially AWS or GCP) for data storage, compute, and deployment
• Version Control & CI/CD: Solid understanding of code versioning (Git) and continuous integration/continuous deployment (CI/CD) processes in a data engineering context
• Agile Methodology: Familiarity with agile development methodologies and ability to work in a fast-paced, iterative environment
• Soft Skills: Excellent communication and presentation skills, with critical thinking and problem-solving abilities
• Data Governance & Ethics: Experience implementing data governance best practices, ensuring data quality and consistency
• Nice to Have: Community & Open Source: Contributions to open-source projects or active participation in data community initiatives
• AI/ML Skills: Experience with applying Artificial Intelligence/Machine Learning techniques in analytics (e.g., building predictive models for forecasting, churn prediction, fraud detection, etc.)
• Statistical Background: Solid foundation in statistics and probability, with ability to apply various modeling techniques and design A/B tests or experiments
• Additional Programming: Knowledge of additional programming or query languages (e.g., R, Scala, Julia, Spark SQL) that can be applied in analytics workflows
• Certifications: Certifications in relevant data technologies or cloud platforms (such as Snowflake, AWS, GCP, or Looker) demonstrating your expertise

Benefits

• Dental insurance
• Vision insurance
• Generous benefits