Job Description We are looking for an experienced data warehousing practitioner to deliver a ramped-up, practical introduction to dimensional modeling and data warehouse concepts for our analytics and data engineering team. This is not a full data warehouse design course and not certification-focused. The goal is to ensure all team members understand the structure they are working with, the data warehouse lifecycle, and core dimensional modeling concepts — so they can reason correctly about data, joins, grain, and analytics behavior. ________________________________________ Context: Why We Need This We are a data & analytics consulting company working on data warehousing, data integration, analytics, and automation. While not everyone on the team designs data models yet, everyone works on top of them: • BI developers build dashboards on dimensional models • Engineers build ETL and automation that feed these models • Analysts validate, reconcile, and explain data A shared understanding of dimensional modeling concepts is critical to avoid incorrect assumptions, broken logic, and rework. ________________________________________ Primary Focus (Ramped-Up / Conceptual) 1. Data Warehouse Lifecycle • From source systems → integration → analytics → consumption • Where dimensional models fit in the lifecycle • Difference between operational systems and analytical systems 2. Core Dimensional Modeling Concepts • Facts vs dimensions • Grain: what a row represents and why it matters • Measures, attributes, and hierarchies • Conformed dimensions and shared definitions 3. Common Dimensional Patterns (Conceptual) • Transaction vs snapshot fact tables • Periodic vs accumulating snapshots • Role-playing dimensions • Slowly changing dimensions (high-level overview) 4. Reading and Reasoning About Existing Models • How to interpret an existing dimensional model • What questions a model can and cannot answer • How modeling decisions affect analytics, BI tools, and performance 5. Common Pitfalls • Incorrect grain assumptions • Over-joining and double counting • Using dimensions as facts (and vice versa) • Why “just add another column” often breaks analytics ________________________________________ Out of Scope for This Training • Full end-to-end dimensional model design • Tool-specific modeling features • Advanced performance tuning • Certification preparation This training is about understanding and reasoning, not building full models independently. ________________________________________ Delivery Expectations • Live, instructor-led sessions • Total number of hours to be agreed after discussion • Sessions must be recorded (screen + audio) — non-negotiable • Recordings will be used internally for training and onboarding • Interactive format with examples, diagrams, and short exercises ________________________________________ Post-Training Assessment Expectation We expect the training to be followed by a short post-training assessment to validate understanding. The assessment should focus on conceptual reasoning and interpretation, rather than tool usage or implementation. Questions should require participants to reason through scenarios, explain implications, or predict outcomes based on the concepts covered. We will collaborate with the trainer to align the assessment format with the training delivered. ________________________________________ What Success Looks Like After the training, participants should be able to: • Explain what a dimensional model is and why it exists • Correctly identify facts, dimensions, and grain • Read and understand an existing data warehouse model • Predict how changes in grain or joins affect analytics • Ask better questions before building or changing anything ________________________________________ Trainer Profile We’re looking for someone who: • Has real-world experience designing or working with dimensional models • Understands analytics, BI, and data engineering perspectives • Can explain concepts clearly without heavy jargon • Is comfortable tailoring content to real project scenarios • Is comfortable with sessions being recorded ________________________________________ Important Notes • Recording sessions is mandatory • This is a ramped-up, conceptual training, not a full modeling course Please include in your application: • A short summary of relevant data warehousing experience • How you would approach a practical introduction to dimensional modelling and data warehouse concepts • Confirmation that recording sessions is acceptable