Program Overview
This high-impact program equips non-engineering professionals with the skills to design, automate, and manage data workflows using GenAI-assisted data engineering tools. Through a blend of foundational concepts, real-world business use cases, and hands-on pipeline-building exercises, participants learn how to ingest multi-source data, clean and transform datasets, generate SQL/business logic using GenAI, and build automated pipelines for downstream dashboards and analytics applications. The program demystifies the modern data stack—covering cloud, ETL/ELT, validation, monitoring, lineage, and AI agents—enabling business users to reduce manual data preparation, improve data quality, and accelerate reporting and decision-making in their functions.
Features
- Build automated, end-to-end data pipelines using GenAI assistants without coding expertise
- Use GenAI to clean, validate, transform and orchestrate multi-source business data
- Generate SQL queries, business rules and transformation logic through natural-language prompts
- Connect pipelines to BI dashboards and insight engines for faster, higher-quality reporting
Target audiences
- Business analysts, reporting specialists & data professionals
- Finance, sales, operations & supply chain professionals
- HR, L&OD, customer success & service delivery professionals
- Strategy, business excellence, risk management & PMO professionals
- IT, digital transformation & data governance professionals
Curriculum
- 6 Sections
- 35 Lessons
- 1 Day
Expand all sectionsCollapse all sections
- Foundations of Generative Data Engineering7
- 1.1What is Generative Data Engineering?
- 1.2GenAI vs Traditional Data Engineering
- 1.3Key Concepts: pipelines, ETL/ELT, semantic layers, data orchestration, connectors, embeddings, vector stores, LLM agents
- 1.4The new data engineering stack: Azure Data Factory, AWS Glue, BigQuery, Databricks, Snowflake + GenAI Assistants
- 1.5Situational Awareness: Why data quality, lineage & governance matter more with AI How non-engineers contribute to the modern data pipeline Data pain points in business functions: fragmented sources, manual exports, inconsistent formats
- 1.6Real Use Cases: Auto-generating SQL scripts with GenAI Automated log cleaning and error detection Sales/Finance teams building pipelines using natural-language prompting
- 1.7Exercise: Identify data-handling tasks in your function that can be automated using GenAI
- Data Extraction & Integration Using GenAI5
- 2.1Automated data ingestion: APIs, connectors, file watchers
- 2.2Natural language data extraction (unstructured – structured)
- 2.3Multi-source integration: ERP, CRM, Excel, PDFs, web data Situational Awareness: Risks of automated extraction: format drift, schema mismatch, incomplete data The importance of metadata and data contracts
- 2.4Real Examples: Finance: Auto-extracting GL, AR/AP, expense reports; Supply Chain: Real-time sensor/IOT ingestion; HR: Policy text → structured datasets; Customer ops: Email → ticket → pipeline
- 2.5Exercise: Use GenAI to convert messy files (PDF/Excel/Text) into structured tables
- Automated Cleaning, Validation & Transformation Pipelines6
- 3.1Transformation logic: joins, merges, pivots, lookups, dedupe, anomaly detection
- 3.2AI-driven validation: semantic checks, consistency rules, outlier tagging
- 3.3Low-code/no-code orchestration flows
- 3.4Situational Awareness: Common transformation failures: type errors, null propagation, multi-format mismatch Data quality scoring frameworks (DQ completeness, accuracy, timeliness, uniqueness, conformity)
- 3.5Real Examples: Manufacturing: automated BOM cleansing Retail: automated promotions data harmonization BFSI: fraud-detection formatting & deduplication HR: workforce data unification from ATS, HRMS, LMS
- 3.6Exercise: Clean a sample dataset using GenAI & rule-based validation and build a transformation pipeline.
- AI-Assisted SQL, Querying & Business Logic Generation5
- 4.1Natural-language-to-SQL (NL2SQL)
- 4.2Query optimization assisted by GenAI
- 4.3Business logic codification: calculated fields, hierarchies, thresholds, aggregationsSituational Awareness: When GenAI-generated SQL failsReading execution plans without engineering skills Ensuring logic is auditable & compliant
- 4.4Real Examples: Auto-generated MTD, YTD, YoY metrics Predictive aggregation scripts Sales pipeline forecasting logic built via prompts
- 4.5Exercise: Instruct GenAI to write SQL queries for real business scenarios – validate output
- GenAI-Driven Pipeline Automation, Monitoring & Alerts6
- 5.1Scheduled pipelines, event triggers, DAGs
- 5.2GenAI agents for monitoring, error detection, pipeline debugging
- 5.3Auto-documentation & lineage mapping
- 5.4Situational Awareness:Avoiding silent failures in automated pipelines How to audit assisted automation Role-based access control & governance
- 5.5Real Examples: Pipeline failures predicted using AI log analytics Automated tests for pipeline health End-to-end monitoring dashboards
- 5.6Exercise: Create a Pipeline Automation Blueprint: triggers, logic, flow, alerting
- Business Apps Using GenAI — Dashboards, Insights & API Workflows6
- 6.1Connecting pipelines to BI tools (Power BI, Tableau, Qlik)
- 6.2LLM-enabled Instant Insights & narrative generation
- 6.3Business apps built over pipelines: forecasting apps, risk scorecards, KPI bots
- 6.4Situational Awareness: Why non-engineers need to understand pipeline – analytics – decision flows Ensuring consistency between pipelines, dashboards & narratives
- 6.5Real Examples: CFO dashboards built with AI-refreshable pipelines Procurement risk bots powered through automated data orchestration Inventory forecasting application using dynamic pipelines
- 6.6Exercise: Build a real-time BI output: Pipeline – Dashboard – Insight Narrative
Instructor
