Analytics Engineer for Narvar focused on building data infrastructure and enabling internal analytics. Collaborating with stakeholders to deliver metrics, models, and self - serve analytics solutions.
About the role
- Analytics Engineer at Tucows utilizing advanced analytics expertise to extract insights from raw data. Collaborating across teams to drive data-driven decision-making and optimize data processes.
Responsibilities
- Design, develop, and maintain complex data models in our Snowflake data warehouse.
- Utilize dbt (Data Build Tool) to create efficient data pipelines and transformations for our data platform.
- Leverage Snowflake Intelligence features (e.g., Cortex Analyst, Cortex Agents, Cortex Search, AISQL) to implement conversational data queries and AI-driven insights directly within our data environment.
- Develop AI solutions that harness these capabilities to extract valuable business insights.
- Design and build advanced SQL queries to retrieve and manipulate complex data sets.
- Dive deep into large datasets to uncover patterns, trends, and opportunities that inform strategic decision-making.
- Develop, maintain, and optimize Looker dashboards and LookML to effectively communicate data insights.
- Leverage Looker’s conversational analytics and data agent features to enable stakeholders to interact with data using natural language queries.
- Communicate effectively with stakeholders to understand business requirements and deliver data-driven solutions.
- Write efficient Python code for data analysis, data processing, and automation of recurring tasks.
- Skilled in shell scripting and command-line tools to support data workflows and system tasks.
- Ensure code is well-tested and integrated into automated workflows (e.g., via Airflow job scheduling).
- Create compelling visualizations and presentations to deliver analytical insights and actionable recommendations to senior management and cross-functional teams.
- Tailor communication of complex analyses to diverse audiences.
- Stay up-to-date with industry trends, emerging tools, and best practices in data engineering and analytics (with a focus on dbt features, Snowflake’s latest offerings and BI innovations).
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.
- Hands-on experience with dbt (Data Build Tool) and its features for building, testing, and documenting data models.
- Expert-level knowledge of data modeling and data warehouse concepts (e.g., star schema, normalization, slowly changing dimensions).
- 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.
- Strong skills in Looker data visualization and LookML (including familiarity with Looker’s conversational AI and data agent capabilities) or similar BI tools.
- 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.
- Experience with real-time data processing and streaming technologies (e.g., Kafka, Kinesis, Spark Streaming) for handling continuous data flows.
- Proficient in Python for data analysis and manipulation (pandas, NumPy, etc.), with the ability to write clean, efficient code.
- 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.
- Experience with cloud platforms and services (especially AWS or GCP) for data storage, compute, and deployment.
- Solid understanding of code versioning (Git) and continuous integration/continuous deployment (CI/CD) processes in a data engineering context.
- Familiarity with agile development methodologies and ability to work in a fast-paced, iterative environment.
- Excellent communication and presentation skills, with critical thinking and problem-solving abilities. Proven track record of working effectively on cross-functional teams and translating business needs into technical solutions.
- Experience implementing data governance best practices, ensuring data quality and consistency. Knowledge of data ethics, bias mitigation strategies, and data privacy regulations (e.g., GDPR, CCPA) with a commitment to compliance.
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development opportunities
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