Professional Certificate in Data Engineering™
Hands-on, industry-aligned training to turn your degree into a job-ready portfolio. Build 6+ real projects, master the tools employers use, and get career support focused on the Australian market.
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Duration: 6 Weeks (Intensive)
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Cohort: 20–25
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Investment: USD $2,500
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Cohort Starting Date: Jan 19 2026
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Outcome: Junior Data Engineer
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Know more about Professional Certificate in Data Engineering
Why students struggle — and how we solve it
We designed the program around the real gaps that block fresh graduates from landing data roles.
Academic Knowledge ≠ Job Skills
Universities teach theory, not the tools and workflows employers expect.Our Solution
- Hands-on labs with real datasets
- Industry-standard tools (Python, SQL, Airflow, Docker)
- Production-style project workflows
No Real Portfolio
Graduates lack projects that demonstrate practical data engineering skills.Our Solution
- 6+ portfolio projects
- Real-world datasets and scenarios
- GitHub-ready code
Limited Industry Connections
Graduates struggle to network and understand job market expectations.Our Solution
- Career coaching sessions
- Industry mentor support
- Job placement assistance
Time to Job-Ready
Self-learning takes too long and lacks structure.Our Solution
- Structured 6-week intensive
- Live sessions + self-paced
- 24/7 mentor support
Everything you need to go from uni to job-ready
Live sessions, self-paced learning, hands-on labs, and real-time mentor support.
Live Weekly Sessions
Interactive sessions with industry experts covering core concepts and real-world applications.
Hands-On Labs
Practical exercises using real datasets and industry-standard tools.
Portfolio Projects
Build 6+ production-ready projects to showcase your skills.
24/7 Mentor Support
Get help whenever you need it from experienced data engineers.
Summary: Roles, pipelines, and data lifecycles; hands-on with relational DBs and core SQL to query structured data.
- Topics: ETL vs ELT, lake vs warehouse, RDBMS intro (PostgreSQL/SQLite), SELECT, WHERE, joins, aggregates, AWS RDS/Azure SQL., Intro to relational databases (PostgreSQL / SQLite)., SQL basics: SELECT, WHERE, ORDER BY, JOINs, aggregations., Cloud database intro (AWS RDS / Azure SQL).
- Project: Build an e-commerce DB and write queries for top products, revenue, repeat customers.
Summary: Master intermediate–advanced SQL, Design efficient relational schemas.
- Topics: GROUP BY, HAVING, subqueries, CTEs, window functions., Normalization (1NF – 3NF), keys, constraints, indexes., ER diagramming (dbdiagram.io / draw.io)., PostgreSQL user management and roles.
- Project: Build a normalized relational schema and analytical queries dashboard.
Summary: Use Python for data collection, cleaning, and automation, Learn basic SQL for querying structured data.
- Topics: Python basics: variables, loops, functions, error handling., Working with CSV/JSON files., Pandas & NumPy for data manipulation., Calling APIs and loading data into databases., Git/GitHub for version control., Linux command-line fundamentals.
- Project: API data collector and cleaner: Fetch API data → clean with Pandas → load to PostgreSQL → push code to GitHub.
Summary: Build ETL pipelines and understand data-warehouse architecture.
- Topics: ETL vs ELT architectures., Data-warehouse design: Kimball vs Inmon methodologies., Star Schema vs Snowflake Schema (dimensional modeling)., Apache Airflow for scheduling/orchestration., dbt for SQL transformations., Data-quality validation (Great Expectations)., Introduction to PySpark for distributed data processing.
- Project: Develop an end-to-end ETL pipeline: extract data → transform (dbt/Pandas) →load into warehouse (Snowflake / PostgreSQL), Implement data-quality tests and schedule with Airflow
Summary: Learn streaming concepts and cloud deployment.
- Topics: Batch vs stream processing., Apache Kafka topics, producers, consumers., Spark Streaming / Flink introduction., Cloud data services (AWS S3, Glue, Redshift / Azure Data Factory, Databricks)., Docker basics and Terraform for Infrastructure as Code., CI/CD for data pipelines.
- Project: Real-time analytics pipeline: Kafka + Python consumer → S3/PostgreSQL storage
Summary: Integrate data engineering with ML pipelines and cover governance.
- Topics: ML pipeline overview (feature engineering, model serving)., MLOps basics (MLflow + Airflow)., Vector databases (Pinecone/FAISS)., Data ethics, bias, privacy, and GDPR compliance., Interview prep: SQL, Python, system design.
- Project: End-to-end Data Engineering Platform: 1. Batch + stream pipelines 2. Cloud data warehouse Portfolio-ready presentation on GitHub.
Capstone Project
Apply everything you've learned in a real-world project.
Architecture
- • Batch ETL with Airflow + dbt
- • Streaming (Kafka → Spark Structured Streaming)
- • Cloud warehouse (Snowflake/BigQuery) + Lake (S3/ADLS)
- • Feature store + basic model serving
Evaluation Rubric
- • Architecture & scalability (30%)
- • Code quality & tests (25%)
- • Observability & reliability (20%)
- • Docs & demo clarity (15%)
- • Ethics & governance integration (8%)
Deliverables
- • System diagram & README
- • Infra as Code (Terraform) & CI/CD
- • Data quality tests (Great Expectations)
- • Demo video (5–7 min) + GitHub repo
Showcase
- • Live demo day with mentors & employer guest
- • LinkedIn post & portfolio review session
- • Optional recruiter panel Q&A
Example Capstone Themes
- • E-commerce analytics: clickstream + purchase pipeline
- • IoT telemetry pipeline with anomaly alerts
- • Marketing attribution with batch + streaming joins
- • FinServ fraud events with near-real-time scoring
You'll pitch your theme in Week 5 and build in Week 6 with mentor guidance.
Portfolio Outcomes
- GitHub portfolio with 3–4 production-level projects + capstone architecture
- Architecture diagrams, documentation, and demo videos
- Verified GlofAI digital badge for LinkedIn & resume
- Technical interview confidence for Australian market
- Lifetime access to alumni/job network (on completion)
- Hands-on ETL pipelines with Airflow & dbt (best practices, testing, docs)
- Cloud deployments on AWS/GCP (S3/BigQuery, IAM, cost-aware architectures)
- Interview-ready SQL/Python challenges repo + mentor feedback loops
Program Support & Career Services
- 1-on-1 mentorship: three sessions with GlofAI-certified mentors (Capped $200 max )
- Resume, LinkedIn, portfolio workshops; one mock technical interview
- Weekly office hours
- 3 months of job search assistance
- Weekly code review with line-by-line feedback
- Industry guest lecture with Australian employer insights
- Application tracker & weekly accountability check-ins
- Hiring-partner intros & alumni referrals (where available)
Why Choose This Program
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Practical Skills:: Real-world portfolio projects build employer confidence.
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Mentorship:: One-on-one guidance from expert data engineers (3 sessions included).
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Career Coaching:: Resume help, LinkedIn optimization, mock interviews, and 3 months job search support.
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AI Ethics:: Learn responsible AI, compliance, privacy, and integrity skills to stand out at interview.
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Australian Market:: Curriculum aligned to local employer needs.
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Local Hiring Practices:: STAR selection criteria, ATS-ready Aussie resume format, and recruiter expectations.
Continue Your Growth
After Week 6, upgrade to :
- Executive Program (12 weeks) — upgrade to accelerate toward Senior Data Engineer. — $4,500
- Advanced Program (24 weeks) — upgrade to accelerate toward Data Architect. — $9,500
Get Hired as a "Junior Data Engineer" — Fast
Our graduates walk away with:
- Resume reviewed by industry experts
- Portfolio proven to impress recruiters
- GlofAI certification trusted by employers
- Direct job leads and alumni network access
- Confidence from mock interviews & coaching
All the Support You Need
Mentors, community, toolchains, and job launch support — all built in.
1-on-1 Mentorship
Personal guidance from pro data engineers every two weeks.
Live Office Hours & Community
Weekly group sessions + 24/7 Discord support — never get stuck alone.
Real Employer Toolkits
Work hands-on with AWS, Azure, Snowflake, Databricks, Docker, dbt, Airflow & more.
Career Launch Services
Resume, LinkedIn, portfolio workshops, mock interviews, and job leads included.
Professional Certificate
Earn an industry-recognized credential and a digital badge you can add to your LinkedIn within minutes of completion.
- Shareable digital badge (Open Badge standard)
- One-click verification for recruiters and hiring managers
- Certificate ID & QR for resumes and portfolios
- Aligned to Australian data engineering job competencies
Voices From Our Alumni Network
Powerful success stories that reflect the value, growth, and real-world outcomes of their journey with us.
GIofAI has completely transformed the way I understand and use AI in my professional life. The guidance, mentorship, and structured learning made complex concepts feel simple and achievable. I felt supported at every step, and the hands-on approach helped me develop real confidence in applying AI tools. This program didn’t just teach me — it empowered me. I’m grateful for the clarity, motivation, and opportunities GIofAI opened for me."
Investment & Next Steps
Seats are limited to maintain quality mentorship and peer support.
- 12 live sessions + 40+ hrs self-paced modules
- 6 projects + capstone; $200 cloud credits
- Career services for 3 months
- GlofAI certificate + digital badge
Scholarships & Payment Plans
We aim to keep the program accessible. Limited scholarships and flexible plans are available.
- Merit-based student scholarships
- Split payments on request
Frequently Asked Questions
Ready to launch your data career?
Apply now or talk to an advisor. Seats are limited.