Senior Analytics Engineer

Company	Lotlinx
Location	Winnipeg, MB, Canada, Hamilton, ON, Canada
Salary	$103000 – $157000
Type	Full-Time
Degrees
Experience Level	Mid Level, Senior

4+ years of relevant professional experience in Analytics Engineering, Data Engineering, or a highly related role, with a proven track record of building and managing complex data systems.
Deep expertise in writing complex, highly performant SQL for data transformation, aggregation, and analysis, particularly within a cloud data warehouse environment like BigQuery.
Demonstrated experience writing, tuning, and debugging complex SQL queries specifically for large-scale data warehouses (multi-terabyte environments), preferably BigQuery or Apache Pinot.
Strong understanding of data modeling concepts (e.g., Kimball, Inmon, Data Vault) and practical experience designing and implementing warehouse schemas.
Proven experience building and maintaining data pipelines using relevant tools and frameworks. Python proficiency for scripting and data manipulation is essential.
Significant experience working with cloud data warehouses, specifically Google BigQuery. Understanding of underlying architecture and optimization techniques.
Excellent analytical and problem-solving skills, with the ability to troubleshoot complex data issues independently.
Strong communication skills, capable of explaining complex technical concepts to both technical and non-technical audiences. Proven ability to collaborate effectively across teams.

Design, develop, and maintain scalable and performant data models in our data warehouse (Google BigQuery, Apache Pinot) to serve as the single source of truth for analytics.
Conduct data validation and exploratory analysis across massive datasets (billions of rows, terabytes) to ensure the integrity of data pipelines and the accuracy of downstream reporting.
Develop, monitor, and troubleshoot ELT/ETL pipelines processing high-volume data streams, ensuring reliability and performance at the terabyte scale.
Optimize complex SQL queries and data transformation logic for maximum performance and cost-efficiency using multi-terabyte datasets within Google BigQuery.
Analyze Apache Pinot query performance logs and usage patterns across terabyte-scale datasets to identify optimization opportunities and troubleshoot complex data access issues.
Partner with data analysts, data scientists, and business stakeholders to understand their data requirements, providing clean, well-documented, and easy-to-use datasets.
Implement data quality checks, testing frameworks, and documentation standards to ensure the trustworthiness and usability of our data assets.
Work effectively within a collaborative team environment. Potentially mentor junior team members and share best practices in analytics engineering.
Keep abreast of new technologies, tools, and best practices in the analytics engineering space and advocate for their adoption where relevant.