Staff Analytics Engineer

Generac Power Systems•Toronto, ON

25d•Hybrid

About The Position

Hi, we are ecobee. ecobee introduced the world’s first smart Wi-Fi thermostat to help millions of consumers save money, conserve energy, and bring home automation into their lives. That was just the beginning. We continue our pursuit to create technology that brings peace of mind into the home and allows people to focus on the moments that matter most. We take pride in making a meaningful difference to the environment, all while being part of the exciting, connected home revolution. In 2021, ecobee became a subsidiary of Generac Power Systems. Generac introduced the first affordable backup generator and later created the category of automatic home standby generator. The company is committed to sustainable, cleaner energy products poised to revolutionize the 21st century electrical grid. Together, we take pride in making a meaningful difference to the environment. Why we love to do what we do: We’re helping build the world of tomorrow with solutions that improve everyday life while making a positive impact on the planet. Our products and services work in harmony to provide comfort, efficiency, and peace of mind for millions of homes and businesses. While we’re proud of what we’ve done so far, there’s still a lot we can do—and you can be part of it. Join our extraordinary team. We're a rapidly growing global tech company headquartered in Canada, in the heart of downtown Toronto, with an office in Leeds, UK, and remote ecopeeps in the US. We get to work with some of North America and UK's leading professionals. Our colleagues are proud to bring their authentic selves to work, confident that what we do is grounded in a greater purpose. We’re always looking for curious, talented, and passionate people to join our team. Who You'll Be Joining The Business Intelligence team designs, maintains and improves the analytics infrastructure that informs decisions across the company. Analytics engineering is a core and growing investment, and this role sits at the center of that work. The Staff Analytics Engineer is a deeply technical individual contributor who owns the transformation layer of the data platform, treats analytical data as a product, and drives how data is modeled, certified, and delivered. You define the standards, reusable patterns, and decision frameworks for the lakehouse silver and gold layers, and raise analytics engineering practice through mentorship and technical leadership This role follows a hybrid schedule, with in-office work required on Wednesdays and Thursdays from our Toronto office to support collaboration, and flexibility to work remotely for the remainder of the week.

Requirements

Deep hands-on DBT expertise: modular, tested, well-documented models; project structure at scale
Strong data modelling skills (dimensional modelling, star/snowflake) and warehouse optimization
Proficiency in Python and SQL for building and troubleshooting transformation code and pipelines
Experience owning a semantic or metrics layer as a shared product that standardizes business logic across tools and teams
Hands-on experience with GCP services, including BigQuery, Dataflow, and Cloud Storage and lakehouse layer interaction
Production experience with orchestration tools such as Apache Airflow or Cloud Composer
Experience designing solutions for both batch and real-time processing
Strong command of data quality and governance, including data contracts, observability, and SLO-driven operations
Practical application of data-as-a-product principles, including discoverability, lineage, quality guarantees, and versioning
Proficiency with CI/CD practices, Git and GitHub, and modular development workflows
Distributed processing familiarity (Spark/Kafka) and credible partnership with data engineering on end-to-end design
Working knowledge of data privacy requirements and technical controls (PII handling, masking, encryption, retention, and access controls)
Familiarity with cloud cost optimization across compute, storage, and query patterns
Proven ability to influence across engineering, analytics, and business through technical credibility and clear communication; ability to explain architecture trade-offs to non-technical stakeholders

Responsibilities

Transformation Layer Ownership Own and evolve the Silver and Gold layers of the lakehouse using dbt
Apply dbt best practices across project structure, modular design, dimensional modeling, testing, and documentation standards
Publish curated, certified datasets (conformed dimensions, core metrics) that are reusable and trusted across teams
Design and maintain the semantic/metrics layer for standardized and consistent self-service analytics Pipelines and Platform Collaboration
Design transformation workflows and contribute to end-to-end data pipelines
Partner with data engineering to align ingestion, schema design, and architecture with analytics needs
Monitor and optimize pipelines for performance, reliability, and cost efficiency Data Quality, Governance, and Reliability
Implement robust data quality frameworks, testing, and observability (e.g., freshness, schema drift, accuracy, anomalies)
Define and maintain data contracts between producers and consumers (e.g., schema, SLOs, quality expectations)
Drive schema change management (versioning, backward compatibility, stakeholder communication)
Design and enforce classification and certification standards so consumers can discover and trust analytical assets
Ensure production reliability through SLO/SLI measurement, actionable alerting, and continuous improvement
Own production operations for critical data flows (incident response, runbooks, root cause analysis) against defined SLOs
Embed privacy and security controls in data models and pipelines (PII handling, masking, access controls, and compliance)
Cross-functional Leadership and Delivery
Drive alignment across Analytics, Product, Engineering, and business stakeholders on analytics data models and platform decisions
Surface technical constraints and trade-offs to inform delivery planning and long-term platform scalability
Translate business requirements into technical specs and deliver consumption-ready curated datasets and semantic models for downstream reporting
Maintain durable documentation for pipelines, models, architecture, and operating processes Technical Leadership and Standards
Define and maintain analytics engineering standards for dbt development (modelling conventions, testing requirements, documentation, review expectations)
Hold code quality to a high bar through structured reviews focused on readability, modularity, testability, and standards compliance
Provide technical mentorship that elevates craft and quality across analytics engineering and analytical work
Evaluate and advocate for tools, patterns, and practices that improve platform reliability, scalability, and maintainability