About the Course
Organizations now expect analytics that can be audited, refreshed predictably, and explained to finance, operations, and leadership without spreadsheet rework. That means you need to demonstrate capabilities in SQL transformation design, dbt model development, warehouse schema design, data quality testing, and pipeline orchestration, all within a governed framework that can stand up to changing source systems and rising AI-ready data expectations. A modern data stack and analytics engineering capability is no longer optional when teams depend on one version of the truth for revenue, operations, and performance reporting.
This course turns scattered knowledge into a working system for modern data stack and analytics engineering. You will practice ELT design, star schema modeling, dbt project structure, Airflow DAG logic, Great Expectations checks, warehouse deployment patterns, and metric documentation workflows. You will also be introduced to adjacent concepts such as reverse ETL, semantic layers, and event-driven data patterns so you can evaluate where they fit in your environment. In plain terms, this course teaches you how to design warehouse-first analytics pipelines, build maintainable dbt models, and validate data quality so your reporting layer stays reliable. The hands-on emphasis is on modeling, transformation, testing, and orchestration; the overview-level content introduces streaming, reverse ETL, and containerized deployment without claiming production engineering mastery in five days.
Many teams also operate under budget limits, mixed tool maturity, and competing reporting priorities, which means the right answer is rarely the most complex architecture. This course is built for professionals who need to deliver in real conditions, using practical patterns that fit typical cloud data warehouses, shared analytics teams, and cross-functional reporting demands. If you need modern data stack and analytics engineering skills that can be applied without overbuilding the stack, this course gives you that operating model.
Target Audience
This course is designed for professionals who already work with data and need to move from ad hoc analysis to governed analytics engineering in modern cloud environments.
- Analytics Engineer responsible for dbt model design and metric consistency
- Data Engineer building ELT pipelines for cloud warehouse transformations
- BI Developer maintaining reliable dashboards and semantic definitions
- Data Platform Analyst managing warehouse tables, tests, and refresh logic
- Reporting Analyst reconciling KPI definitions across business teams
- DataOps Engineer automating deployment, testing, and monitoring workflows
- Analytics Manager overseeing transformation standards and reporting reliability
- Head of Analytics aligning warehouse outputs with business performance needs
- Business Intelligence Lead translating models into decision-ready data products
- Data Governance Specialist checking lineage, quality, and metric control
Course Objectives
This course equips you to plan, build, and measure modern data stack and analytics engineering initiatives that improve reporting reliability, support governance, and strengthen data-driven decision-making.
- Assess current warehouse readiness using the Medallion Architecture and dbt project structure.
- Apply ELT transformation patterns to design maintainable SQL models for analytics use cases.
- Build star schema and dimensional models that support consistent KPI reporting in Snowflake or BigQuery.
- Create dbt tests, documentation, and modular models to improve transformation quality.
- Evaluate pipeline reliability using Great Expectations checks and Airflow run status.
- Navigate data governance requirements with lineage, metric definitions, and semantic layer controls.
- Implement refresh and deployment workflows using Git-based version control and CI/CD practices.
- Synthesize transformation results into a KPI dashboard, model catalog, and analytics roadmap.
Requirements & Prerequisites
Prerequisites required: working knowledge of SQL joins, aggregations, and window functions; basic familiarity with Python; and a practical understanding of reporting or warehouse-based analytics. Prior exposure to cloud data warehouses, Git, or dbt is helpful but not mandatory. A laptop is required for hands-on labs, and participants should be prepared to run browser-based tools and follow guided exercises using provided sample datasets.
Local Application and Business Return in Guinea
How participants can apply the training in local operating conditions, and the return their organisation can plan for.
How participants apply this
Expected ROI
Training Methodology
This is a practical, outcome-driven course designed to turn modern data stack and analytics engineering aspiration into measurable action and credible reporting.
Methodology includes:
- Hands-on SQL exercise using warehouse transformation metrics and sample fact tables.
- Scenario simulation for delayed source feeds, broken dependencies, and failed dbt runs.
- Diagnostic review using the dbt testing framework and Great Expectations checklist.
- Stakeholder mapping for analytics owners, data engineers, BI users, and governance reviewers.
- Case study analysis drawn from retail, financial services, SaaS, and manufacturing data stacks.
- Group workshop producing a warehouse modeling blueprint under time and budget constraints.
- Reflection exercise using benchmarked KPI drift, lineage gaps, and refresh latency evidence.
Upcoming Sessions
Next available dates worldwide
No international sessions scheduled
Certification
Recognized credentials that advance your career
Participants who complete the Modern Data Stack and Analytics Engineering Training Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.
NITA Accredited
Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.
CPD Certified
Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.
Why this course earns its place on your CV
Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.
Effective Learning & Skill Development
- Build expertise with structured, outcome-driven learning.
- Equip individuals and teams with skills that grow with industry needs.
- Reinforce learning through real-world scenarios, case studies and practical exercises.
Career Growth & Professional Advancement
- Apply what you learn with a proven methodology that ensures lasting impact.
- Develop immediately usable skills that translate directly into workplace success.
- Gain the expertise needed for career advancement and leadership roles.
Training Optimization & Learning Excellence
- Tailor training to industry-specific challenges and organizational goals.
- Use data-driven insights and automation to enhance training effectiveness.
- Evaluate progress and ensure long-term learning success.
Tools and platforms relevant to this field
Examples Guinea teams may encounter, and that may be featured in training where they support the confirmed course scope.
These are field-relevant examples, not a promise that every tool will be covered. Exact coverage depends on the confirmed course scope, participant needs, and delivery format.
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Power BI MicrosoftUsed to build and distribute governed dashboards and reports from curated warehouse models.
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Apache Airflow Apache Software FoundationUsed to orchestrate ELT workflows, schedule refreshes, and manage dependencies between data jobs.
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Snowflake Snowflake Inc.Used as a cloud data warehouse for centralized analytics, transformation, and sharing of trusted datasets.
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BigQuery GoogleUsed for scalable warehouse-native analytics on cloud data with fast SQL transformations and reporting.
