Senior Analytics Engineer

Lightspeed
Toronto, Ontario, Canada$125k – $135kPosted 11 March 2026

Job Description

Lightspeed Financial Services is growing quickly and we are looking for a talented and passionate Senior Analytics Engineer to join our Financial Services Data team. This is a great opportunity to be a part of an early team and help define the data landscape for very fast-growing, global Financial Services products. If you have worked on data pipelines, have great communication skills, are proficient at data modelling, and have enabled teams to gather both predictive and historical insights, you might be a great fit for this role! This role also involves collaborating with multiple functions across Lightspeed, including Application Engineering, Product, Operations, Risk and Finance, among others. What you’ll be responsible for: Build Scalable Data Pipelines: Design, develop, and optimize robust data pipelines, making data easily accessible for visualization and machine learning applications Cleanse and transform raw data into business logic to help stakeholders build reports and data models Ensure the right data gets to the right people at the right time Collaborate with the Central Data Team to build data pipelines in accordance with shared best practices and design, and feed proper data models for usage Act as a champion for data quality and usability across the Financial Services organization Ensure availability, quality and searchability of data and documentation Write, clean and maintainable queries and scripts that are highly performing, well-structured and easy to understand and extend Automate testing of data models and add appropriate validations to ensure quality Develop and maintain scalable dashboards and reports using Looker Ensure data accuracy, report performance, and a seamless user experience while adhering to BI governance standards Troubleshoot and resolve reporting or data-related issues and contribute to process improvements Help define and improve our internal standards for data models and infrastructure Help define the data vision and roadmap for a new and fast-growing product Coach/Mentor junior AE's to help with their upskilling and foster a learning environment What you will bring to the team: Proficient with SQL and Python (or equivalent language) Experience with MPP data warehouse (we use BigQuery) Experience with Kimball/Star-schema data modelling implementation Proficiency in performing data profiling, data mapping and data quality validation Experience with data orchestrator Solid understanding of industry standards for data warehousing and data modelling/ DBT/Dataform Proficiency with data environments (we use GCP) as well as containerization solutions Proficiency in source control, CI/CD and automated testing A security mindset with the ability to work with compliance protocols (SOX, GDPR) Excellent communication skills and ability to communicate with technical and non-technical stakeholders Excellent problem-solving skills Ability to work independently and in teams Self-motivated and great at taking initiative Some additional things that would be a bonus : Experience in the Payments or Financial Services domain Experience in working with SaaS solutions Solid knowledge of cloud services (AWS and GCP in particular) What’s in it for you: Join a growing team and help us move to the next level Amazing benefits perks, including equity for all Lightspeeders Constant development of both your skill-set and business acumen with limitless growth opportunities Lots of autonomy, flexible work culture Innovation time to explore and learn at work Shaping the company by joining cultural technical committees Tons of growth opportunities into technical or people management roles Opportunity to join a fast-paced, high-growth company At Lightspeed, we carefully consider a wide range of factors when determining compensation, including your skill set, qualifications, experience, and market data. These considerations can cause your compensation to vary. We reasonably expec ... (truncated, view full listing at source)
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