• Develop and maintain robust and scalable data pipelines to extract, transform, and load (ETL) data from various sources into our data warehouse. • Design, build, and optimize data models to support analytical needs and reporting requirements. • Collaborate with data scientists to implement and deploy machine learning models and analytical solutions. • Monitor data quality and identify and resolve data integrity issues. • Contribute to the development of data documentation and metadata management practices. • Work with stakeholders to understand their data needs and translate them into technical requirements. • Assist in the evaluation and implementation of new data technologies and tools. • Participate in code reviews and contribute to the development of coding standards and best practices. • Independently manage assigned tasks and projects, ensuring timely delivery and quality results. • Proactively identify opportunities to improve data processes and systems.

Role Description Flexion is looking for an Analytics Engineer for an enterprise-level application development project to help modernize data infrastructure and transform reporting and discover insights for one of the nation's largest pork producers. In this role, you must possess excellent problem-solving skills and deep technical knowledge, with a strong aptitude for understanding the focus, scope, and impact of your work in a broader, forward-looking software landscape. We are looking for an Analytics Engineer with strong data modeling skills and hands-on experience building reports and dashboards that real business users rely on every day. It is important to use test-driven development with full code coverage. This team works in close collaboration with business stakeholders, iterating quickly based on direct feedback from the people who depend on the data every day. - Design small pieces as you go, while still thinking about how they fit into the whole. - Welcome change as a positive thing and never look at anything you deliver as “final.” - Understand when “good enough” really IS good enough, without compromising on overall product quality. - Focus on team health, communications, and problem-solving to create a good working team. Qualifications - 5+ years of analytics engineering or BI development experience, ideally in agricultural production, supply chain, or enterprise operations. - BS (or higher) in Computer Science, Data Engineering, Data Science, or a related field. - Proficiency in data modeling for analytics — dimensional modeling, star schemas, and building report-ready data marts. - Proven experience building operational and financial reports and dashboards that business stakeholders rely on for day-to-day decisions. - Experience with dbt (data build tool) or similar transformation frameworks. - Strong proficiency in SQL for complex data transformations and operational reporting. - Hands-on experience with AWS QuickSight or Power BI, including dataset design, calculated fields, and dashboard publishing. - Knowledge of AWS cloud services (S3, Glue, Aurora, Athena, CloudWatch). - Familiarity with data lakehouse architectures and cloud-native ETL/ELT patterns. - Experience integrating data from multiple third-party vendor APIs and disparate source systems. - Self-motivated with strong communication skills for cross-functional collaboration with non-technical stakeholders. - Passion for learning new technologies and new business domains quickly. - Ability to write high-quality, testable code with comprehensive error handling. - Experience with Agile application development. - Test-Driven Development mindset for data pipeline reliability. - Experience with data quality validation, reconciliation processes, and error handling patterns. Benefits - Comprehensive health, dental, and vision coverage. - Life and disability insurance. - Flexible spending accounts. - 401(k) plan. - Generous paid time off. - Referral bonuses. - Commitment to well-being and career success. Company Description At Flexion, our company culture is built on autonomy, trust, and transparency. We empower teams to remain autonomous and self-directed by hiring people who can solve complex problems collaboratively. Every member within a cross-functional team is a leader who takes responsibility for the entire team’s success, which mirrors the company’s overall approach of low hierarchy. We believe that growth comes from working together, supporting each other through thoughtful, constructive feedback. - Speak openly and honestly with your colleagues and clients about problems and proposed solutions. - Welcome and handle changing requirements and priorities with little or no warning. - Collaborate online in small groups about 50% of the time. - Encourage simple and minimal solutions that keep options open. - Expect and vocally advocate for quality first. - Learn new practices and techniques as the situation demands. - Ensure the teams develop demonstrable software every week or two. - Do what needs to be done to deliver the product or project without ego or attitude. - Dig deep to find the root causes of problems to create the right solutions. - Relentlessly improve yourself, your team, and your processes. - Offer and welcome constructive feedback as a tool for continuous team and individual growth. - Embrace AI assistants (e.g., ChatGPT, NotebookLM) as force multipliers to accelerate innovation, iterate faster, and maintain high-quality output.

Role Description Are you someone who thrives on helping others succeed, enjoys making an impact, and takes pride in guiding customers to the right solutions for their projects? If you’re also naturally curious and eager to keep learning, consider starting or growing your career with us at The Home Depot. Our entrepreneurial spirit and innovative mindset, combined with the resources and support of The Home Depot Canada—the world’s leading home improvement retailer—offer a unique opportunity to join a transformative, data-driven organisation. You'll be the data layer for the entire company — a strategic partner to Finance, Marketing, Sales, Product, Supply Chain and Data Science — designing the clean, trusted datasets that power our traditional BI and next generation AI tooling and ensuring every team can make decisions from a single source of truth. This role is highly focused on data modelling, analytical thinking, and business-facing design, rather than pure pipeline engineering. You will work at the intersection of business requirements, data domain knowledge, and analytics consumption, ensuring that data structures are intuitive, performant, and aligned to enterprise standards. The Analytics Engineer is critical to ensuring that inventory data is modelled once and used consistently across the organisation, enabling trusted reporting, faster insights, and scalable analytics. The successful candidate will play a key role in shaping how inventory data is structured, governed, and consumed across business and analytics teams. Qualifications - Bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field. - 3-5 years of experience in data analytics, data modelling, or analytics engineering roles. - Demonstrated experience designing enterprise-scale analytical and reporting data models. - Experience working in retail, inventory management, or supply chain analytics environments is a strong asset. Requirements - Build and own the canonical data models in Big Query that serve as Home Depot Canada’s company-wide source of truth — clean, queryable, and AI-ready. - Partner with business stakeholders, analytics leads, and data engineering teams to gather and clarify reporting and analytical requirements related to data, translating business questions into well-defined analytics and data modelling requirements. - Structure datasets so vendor AI tools perform optimally out of the box, with consistent schemas, rich semantics, and well-indexed access patterns. - Design, document, and recommend logical and physical data models for data stored in Google BigQuery, optimised for analytics and reporting use cases. - Design data models with clear entity relationships, metadata, and business definitions. - Anticipate future AI-driven needs by modelling data with high data quality, interpretability, historical tracking, and feature reusability in mind. - Define and implement dimensional data models (facts, dimensions, conformed dimensions, hierarchies) that support reporting, trends, and performance analysis. - Partner with data architects to inform the design of a semantic/reporting layer in BigQuery by applying dimensional modelling (curated marts, star schemas, conformed dimensions, hierarchies) and publishing governed, reusable datasets (e.g., standardised views and materialized views) to enable consistent metrics and self-service analytics. - Partner with data engineers to ensure models are performant, scalable, and cost-efficient within BigQuery. - Establish and promote best practices for data modelling, metric definition, and semantic consistency across analytics teams including eliminating shadow tables and one-off datasets by proactively serving team data needs at the platform level, freeing data science to move faster. - Support analytics and reporting teams by enabling clear interpretation of reporting metrics and data structures. - Document data models, assumptions, definitions, and lineage to support governance, onboarding, and long-term maintainability. - Act as a data modelling and analytics subject matter expert, providing guidance and recommendations across cross-divisional initiatives. Benefits - The pay range for this position is between $62,200.00 - $70,100.00. Core Competencies - Good judgment: You scope well, prioritize ruthlessly, and communicate trade-offs. - Analytics & Business Acumen: Strong ability to translate business and reporting needs into scalable data structures. - Semantic Layer Design: Experience designing semantic models that power BI tools and self-service analytics. - Collaboration: Proven ability to work closely with business partners, analytics leads, and engineering teams. - Problem Solving: Analytical mindset with a focus on simplifying complexity and enabling insight. - Data Quality & Governance: Strong focus on metric consistency, definitions, and trusted reporting. Technical Skills - Advanced SQL with strong experience in Google BigQuery. - Hands-on experience designing fact and dimension tables, star schemas, and analytical models. - Experience working with inventory, supply chain, or retail domain data (strongly preferred). - Familiarity with semantic layers and reporting tools (e.g., Looker, Tableau, Power BI, or equivalent). - Understanding of performance optimisation and cost considerations in cloud data warehouses. - Ability to partner effectively with data engineering teams on implementation and optimisation. Soft Skills - Strong stakeholder communication skills, with the ability to explain complex data concepts in business-friendly language. - Excellent documentation and requirement-gathering skills. - Ability to work independently while collaborating across multiple teams. - Detail-oriented with a strong sense of ownership and accountability.

• Configure and maintain GA4 across Nexus client and Foundry for Good properties, including migration cleanup • Implement event tracking, custom events, conversions, and goals • Set up and manage Google Tag Manager, including data layer configuration • Build Looker Studio dashboards and reporting as needed • Handle cross-domain tracking, consent mode, and server-side tagging where required • Debug and resolve tracking issues to ensure data accuracy and reliability • Document tracking setups and implementations for internal teams • Join client calls to explain implementations and answer technical questions (a big plus) • Collaborate with account, SEO, and analytics teams who handle downstream analysis