George Firican

The University of British Columbia

Director, Data Governance & Business Intelligence

Led a team of wonderful and highly skilled people in the areas of:
- Finance
- Human Resources
- Information Systems
- Gift Processing 
- Operations
- Strategic Projects
- Prospect Research & Analytics 
- Data Governance & Business Intelligence 

To support the goals of the fundraising and alumni engagement portfolio across the university.

Chief Advancement Operations Officer (Interim)

Director of Information Technology (Interim)

• Lead the Data Governance/ Quality program and the strategy development of Development and Alumni Engagement’s (DAE) data acquisition, maintenance and dissemination while fostering innovation and long term strategic vision
• Lead the efforts to develop a business glossary as well as a business intelligence (BI) framework and program that provides user-friendly access to the right information for the right people at the right time
• Directed the development and enhancement of standards, procedures, processes, policies related to the improvement and/or maintenance of quality of data within the Blackbaud Enterprise CRM database
• Created and fostered strategic partnerships and influential relationships with DAE senior management, the broader UBC community, campus stakeholders, vendors, and external universities to secure the success of DAE’s data governance, business intelligence and information quality efforts
• Collaborated with Information Technology to manage the development of automated data validations, data cleanses, data quality issue reports, and deployment of new tools and enhancements to aid in the preservation of data quality
• Advanced the data governance and business intelligence profession by participating in professional organizations, acquiring or maintaining required accreditations, publishing articles, and giving presentations at professional events

Associate Director, Data Governance & Business Intelligence

I proudly took part of UBC's "Start an Evolution" campaign (https://startanevolution.ubc.ca/), campaign which raised $1.5 billion dollars for students, research and community engagement and to double the number of alumni involved annually in the life of the university by 2015.

• Established and led an award winning Data Governance/Quality Program to support the university’s $1.5 billion fundraising campaign and sustainable alumni engagement strategy
• Provided oversight of the Data Governance/Quality Program and kept it on track by managing the development of standards, procedures, processes, policies related to the acquisition, management, and dissemination of enterprise data impacting the Blackbaud Enterprise CRM database while leading the Advisory Council
• Developed an award winning Tracing Program through which the returned mail percentage of Development and Alumni Engagement’s Portfolio has been reduced to less than 1%
• Managed the development of business performance metrics and scorecards to monitor and communicate the status and improvement of service of the Records department
• Achieved a 92% address and phone number accuracy for Canadian records, an improvement from ~64%
• Communicated to and educated internal and external stakeholders on the results and initiatives of data governance, quality, master data management, information delivery and advised stakeholders about best practices; was the first and only department to achieve 100% on the annual client satisfaction survey on communication delivery and content

Data Quality Manager

LightsOnData

- Educational Content Creation: Develop and deliver comprehensive courses, workshops, and webinars on data governance, data management, data quality, AI, and more.
- Brand Amplification: Collaborate with data companies to enhance their brand visibility and thought leadership in the data community.
- Engagement and Outreach: Host the Lights On Data Show podcast, featuring industry leaders and experts to share their insights and experiences.

Founder

Pearson

Course Instructor

O'Reilly

iData Quality Academy

Developed course content for iData Quality Academy
- Data Profiling
- Data Quality Costs

Instructor

Self employed

• Provided project management and business analysis expertise to various projects across the different industries (BaniPePost.com, JT Business Group, SMAWS, 8p8, Ashenzi, NetPress Consulting, LTS, AHACommunity, etc).
• Designed and developed unique, highly professional web and print designs, branding and e-learning solutions as well as developing and configuring custom websites (e-stores, online communities, content management systems, blogs, brochure websites, etc.).
• Accumulated experience as a web master by installing, upgrading, performing backups, managing migrations and maintaining a series of complex websites.

Freelance Consultant

Sauder School of Business, UBC

• Served as internal Project Manager for the evaluation, build and deployment phases of a custom-built, scalable web payment gateway (WPG) that complied with the PCI standard requirements and reduced Sauder Finance’s operational time for the allocation and reporting of revenue received via card payments by 75%
• Managed the deployment of a FIPPA compliant e-newsletter tool (Industry Mailout) selected to achieve a standard for design and operational support while reducing expenses by $15k/year
• Planned and managed the development and release of a new service for personal/academic blogging through the use of the WordPress MU environment for faculty members and current Sauder students
• Managed the development of an improved content publishing process to account for 15 digital signs, each with audience specific content & emergency broadcasting capabilities
• Served as the internal project manager for the development and deployment of the Canaccord Learning Commons web presence comprised of a WordPress based website and a BuddyPress student interaction environment
• Developed solutions for integrating the business environment with web 2.0 technologies such as WordPress 3 based sites, online communities (Ning, Moodle), and collaboration tools (MediaWiki, Project Spaces, CoMapping) while providing expertise and system support for these platforms
• Identified customer requirements, analyzed and reviewed product features, integration, security, scalability and performance requirements for projects such as PPM Tool, a collaboration tool to be used between the Center of Health Care Management and BC health care authorities, Desktop Blogging Client Solution, ECM Society Online Presence, Faculty Master Calendar, Form Building Tool, Event Registration, PCI Payment Forms Conversions, and several online presences for different business areas and conferences

Project Manager / BA

Everest iTech

• Promoted to Project Manager for the Showtime Models website and web application and the All Ears accounting software development project due to excellent interpersonal, communication and leadership skills in the relationships with the client and internal development team
• Led the development of several e-commerce sites (ex: SkyHook Solar, Expressive Designs Florist, JT-Autotech) and dynamic websites (ex: Mailinglist System, Informant Zone) and also helped architect their databases
• Increased the employees' knowledgebase by developing e-learning modules and user manuals for the use of the company’s payroll and accounting software
• Enhanced the online awareness of the company’s brand by 40% by creating a new content strategy and redeveloping its website in a WordPress, SEO friendly environment
• Minimized the cost of online advertising by developing free distributed widgets that increased incoming traffic by 5k visitors/ month

Project Manager / Lead Web Developer

Event Technologies

• Developed customer support standards and procedures for the company’s e-store business and helped transition a staff of 12 to this new environment through training sessions and a knowledgebase wiki.
• Minimized the installation time of new systems by 80% by creating and managing standard desktop images based on the employees’ job roles.
• Reduced search time of hardware and software information by 50% by developing a standard for filing all the technical documentation on hardware equipment and software.

IT Consultant

WGOA

• Demonstrated strong leadership skills by acting as the team leader of a software developer group in order to successfully implement and deliver an interactive Go learning application two weeks ahead of deadline and within budget.
• Coded the backend interaction of a merchandise inventory application with a barcode scanner and also contributed to developing the MySQL database and reporting. 
• Facilitated the team members to work remotely by implementing a VPN solution and integrated the use of Subversion to enable software versioning and revision control.

Software developer

💬 Founder of LightsOnData.com and LightsOnData YouTube Channel
Focused on providing informative content such as ONLINE COURSES, templates, guides, best practices, articles, white papers, and other useful resources to help you with your data governance and data management questions and challenges

💬 Creator of online courses that are engaging, new, and thorough. 
Courses: Building an Award-Winning Business Glossary, Data Governance Maturity Models, Data Governance Fundamentals, etc. - check them out here: https://www.lightsondata.com/courses/

💬 Providing LinkedIn content strategy for data-focused companies and products, as well as those with a focus on artificial intelligence (AI), business intelligence (BI, data science, data management, and data governance areas.
Connect with me today and ask how I can help you reach the data audience.

💬 Host of Lights on Data Show - a podcast and LinkedIn Live weekly show that provides insights into the data world and showcases people that help to shape it.

💬 Get some fun data merchandise here: https://shop.spreadshirt.com/lightsondata/all

💬 International key note speaker, conference speaker, and panelist (spoken at over 60 different conferences).

💬 I love being on camera, hiking, surfing, filming, and video editing
YouTube: @LightsOnData


🏛 My experience and specialties: 
• Expertise in data governance, information quality, business intelligence & data management
• Extensive background in web and application development with experience of the full life-cycle of a project
• Strong leadership capabilities, organizational and communication skills

Specialties: 
• Enterprise information management: Information quality, data governance and stewardship, master data management, business intelligence, business process improvement
• Development: Web and application development, relational database design and development
• Planning: Budget and resource planning, agile and waterfall project management, team development and leadership
• Tools and Applications: Collibra, Tableau, MS Visio, MS Project, Premiere, Photoshop

George Firican 💡

Here is my recommendation for this week.
If Agentic AI is on your roadmap, checkout this ebook  👇

It breaks down what many overlook: AI success depends on the maturity of your data architecture.
Here's what else you can find in it:
• Clear explanation of what a modern, AI-ready architecture looks like
• Practical guidance on data quality, cataloging, lineage and governance
• A real example of how a bank scaled agentic AI by modernizing its foundation

So if you are leading data initiatives or planning for AI adoption, this is a good resource to read.

Until next time, let's keep putting the Lights on Data!
-George 
#data #AI #partnerpost

💡 Award Winning Data Governance Leader | Content Creator & Influencer | Founder of LightsOnData | Podcast Host: Lights On Data Show | LinkedIn Top Voice

Modernize Data Architectures for  Agentic AI Success

What are the biggest misconceptions business leaders still have about AI?

I sat down with Stijn (Stan) Christiaens, Co-founder of Collibra, at Data Citizens to unpack what AI readiness really means for organizations. 

Here are 5 takeaways from our chat:

1. Many leaders still see AI as “a chat in the cloud.”
2. Organizations need the right foundations around AI
3. AI ready data requires more than quality
4. Unstructured data is no longer optional
5. Unified governance is becoming essential

If you want a clear picture of what it truly takes to prepare your organization for AI, this interview delivers.
Full conversation in the video below.

Until next time, let’s keep putting the Lights On Data.
-George Firican

#data #datagovernance #AI

Every December I look ahead… and this year, one prediction feels very safe:

I'll be attending Subsurface Live on December 10, coffee in hand, curious about how Agentic AI and open lakehouse patterns will shape 2026.

The agenda lines up well with questions many of us are exploring right now:
➡️ How will agent-based execution reshape data workflows?
➡️ What signals are emerging around performance, formats and multi-cloud patterns?
➡️ Where is the Apache Iceberg ecosystem pushing innovation next?

For anyone designing data systems or planning next-year priorities, this feels like a valuable moment to absorb ideas and future direction.

Registration is free: https://lnkd.in/gmDPiaES
Where: Virtual
Date: Dec 10, 2025 
Time: 8AM - 2PM PT / 4PM - 10PM GMT / 5PM - 11PM CET

See you there!

#data #AI

Wavicle Data Solutions

Lead Data Engineer

LinkedIn

LinkedIn Learning Instructor

Globant

Senior Data Engineer

CRISIL Limited

Senior Analyst - Data Engineer

Quantiphi

Data Engineer

Tata Consultancy Services

System Engineer

Assistant System Engineer

Assistant System Engineer - Trainee

InsideSherpa

KPMG Data Analytics Consulting Virtual Internship

Monisha Enterprises

•	Data mining and Extraction Transformation Loading using R programming.
•	Data cleaning and processing for Data analysis done using Python using NumPy, pandas, SciPy, Scikit. Data modelling and prediction was done using algorithms – Logistic Regression, Time Series Forecasting, Clustering and Classification.
•	Data Visualization done using Tableau for generating reports to enhance the productivity of the products.
•	Built a Data Warehouse on MSSQL Server for ERP System of the enterprise to organize the data for future processing.

Data Analyst Intern

Open to Collaborate | Storyteller | Lead Data Engineer@Wavicle| Linkedin Top Voice 2024 | Globant | Linkedin Learning Instructor | 2xGCP & AWS Certified | LICAP'2022

Data Engineer with 7+ years of experience working on data engineering, analysis, and modelling projects for various domains, such as banking, healthcare, sales, and e-commerce. I leverage my strong statistical and analytical ability to explore business ideas and provide innovative solutions. 

Skilled with technologies and strong intellect with enthusiasm as an avid learner with challenging opportunities. I’m working as a LinkedIn Learning Instructor to bring change as a data evangelist.

Post Graduated in Master of Technology in Data Science and Engineering from Birla Institute of Technology and Science, Pilani in 2023, where I learned advanced concepts and techniques in data science and engineering, machine learning, cloud computing, and visualization. 

I hold multiple certifications in GCP, AWS, and Azure, and I enjoy working with cloud technologies and big data platforms.

I am passionate about data science and women empowerment, and I have been recognized as the 
- Linkedin Top Voice 2024 
- Data Science and Women Empowerment Thought Leader by Global AI Hub. 

I am also a proud participant of the LinkedIn Creator Accelerator Program 2022, where I share my insights and experiences with the data science community. 

I am open to collaborate and share ideas, or explore various opportunities in the data field.

Pooja Jain

When everyone talks about data… but only one person actually fixes it. 

👉 It’s always the Data Engineer who climbs into the pit and makes the system work.

If you’re starting your journey as that person — the quiet builder behind every AI success — here’s a structured reading list and GitHub resources to build rock-solid foundations.

1. 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀
 • Tools: Excel, Power BI, SQL, Python
 • Focus: Automate reports, clean data, build dashboards
 • AI Boost: Use Copilot or ChatGPT to write Python scripts, generate SQL queries, and debug faster

2. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺
 • Tools: Pandas, dbt, Git, Jupyter
 • Focus: Write modular code, version control, transform data
 • AI Boost: Let AI help refactor messy code, explain Git workflows, and generate dbt models

3. 𝗗𝗮𝘁𝗮 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 
 • Tools: Airflow, Prefect, dbt, Databricks
 • Focus: Build ETL pipelines, schedule jobs, test transformations
 • AI Boost: Use AI to design pipeline architecture, write DAGs, and troubleshoot errors

4. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗖𝗼𝗿𝗲
 • Tools: AWS/GCP/Azure, Spark, Kafka
 • Focus: Scale workflows, handle big data, deploy in cloud 
 • AI Boost: Use AI to generate infrastructure-as-code, optimize Spark jobs, and simulate Kafka streams

5. 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 & 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀
 • Tools: GitHub, Streamlit, FastAPI, LinkedIn
 • Focus: Build end-to-end projects, document, share, and network
 • AI Boost: Generate project ideas, write documentation, and create interactive dashboards

But Will AI Replace Data Engineers❓
It’s a common fear. But here’s the truth:
AI won't take your job; 
it will just automate the boring parts, raising the floor for everyone else.

Explore these data engineering projects to upskill and level up-

Beginner:
ETL pipeline using Python and SQL by Ankit Bansal:
- https://lnkd.in/gTdCV9aJ
- https://lnkd.in/gxEYM3Bb

Intermediate:
- Data warehouse solution using Snowflake, dbt by Shashank Mishra 🇮🇳 : https://lnkd.in/gf-c5TR7
- Mr. K Talks Tech : https://lnkd.in/gt9JAkRt
- Snowflake project by Data Engineering Simplified : https://lnkd.in/gXRWyHpc
- Apache Spark project by Ankur Ranjan: https://lnkd.in/gt9JAkRt

Advanced: Implement a real-time streaming data processing
- Darshil Parmar : https://lnkd.in/ghiEsa7P
- Yusuf Ganiyu : https://lnkd.in/giYwJaCS

Cloud Projects:
- Microsoft Azure by Data Engineering Simplified - https://lnkd.in/gx3aqzKU
- Microsoft Azure by Sumit Mittal - https://lnkd.in/dX2yma5b
- Amazon Web Services (AWS) by Darshil Parmar - https://lnkd.in/gJg7KV-7
- Google Cloud by Anjan - https://lnkd.in/gjHbmCaM

System Design concepts with:
- Alex Xu - https://lnkd.in/gGdgJRDd
- Design Gurus - https://lnkd.in/gaphzp89

DataExpert.io handbook compiled by Zach Wilson -https://lnkd.in/gb4xBQJy
🔥 𝗔𝗜 𝘄𝗼𝗻'𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀—𝗶𝘁 𝘄𝗶𝗹𝗹 𝗺𝗮𝗸𝗲 𝘁𝗵𝗲 𝗴𝗼𝗼𝗱 𝗼𝗻𝗲𝘀 𝘂𝗻𝘀𝘁𝗼𝗽𝗽𝗮𝗯𝗹𝗲 𝗮𝗻𝗱 𝗹𝗲𝗮𝘃𝗲 𝘁𝗵𝗲 𝗿𝗲𝘀𝘁 𝗯𝗲𝗵𝗶𝗻𝗱.

From 'What does that even mean?' to 'I got this' in 30 terms!

Data Engineering isn't about tools; it's about understanding the language. 

Master these concepts, and you’ll see the entire system relatable, not just the pipeline parts.

𝗗𝗔𝗧𝗔 𝗠𝗢𝗩𝗘𝗠𝗘𝗡𝗧
→ ETL vs ELT: Cook before serving vs serve then cook. ETL transforms first, ELT loads raw and transforms later.
→ Stream vs Batch: Live TV (process as it arrives) vs binge-watching (scheduled chunks).
→ Data Pipeline: Automated highway moving data from source to destination.

𝗗𝗔𝗧𝗔 𝗦𝗧𝗢𝗥𝗔𝗚𝗘
→ Data Warehouse: Organized filing cabinet—structured, optimized for analytics (Snowflake, BigQuery).
→ Data Lake: Your attic—stores raw everything: CSVs, logs, videos. Cheap but messy.
→ Data Lakehouse: Lake affordability + warehouse reliability (Delta Lake, Iceberg).

𝗗𝗔𝗧𝗔 𝗦𝗧𝗥𝗨𝗖𝗧𝗨𝗥𝗘
→ Star Schema: Central fact table + dimension tables. Simple, fast.
→ Snowflake Schema: Normalized star schema—saves storage, complicates joins.
→ Data Modeling: Designing table relationships so queries don't become nightmares.

𝗗𝗔𝗧𝗔 𝗣𝗥𝗢𝗖𝗘𝗦𝗦𝗜𝗡𝗚
→ OLTP: Real-time transactions (your bank app).
→ OLAP: Analytics on millions of rows (your dashboard).
→ Partitioning: Slice tables by date/region so queries don't scan everything.
→ Sharding: Split data across servers—multiple checkout lanes.

𝗗𝗔𝗧𝗔 𝗗𝗜𝗦𝗖𝗢𝗩𝗘𝗥𝗬
→ Data Catalog: Library index—find datasets, understand metadata.
→ Data Lineage: Family tree—track where data came from and where it's going.
→ Indexing: Shortcuts so databases don't scan every row.

𝗤𝗨𝗔𝗟𝗜𝗧𝗬 & 𝗚𝗢𝗩𝗘𝗥𝗡𝗔𝗡𝗖𝗘
→ Data Quality: Accurate, complete, consistent—garbage in = garbage out.
→ Data Governance: Access rules, security, compliance.
→ Caching: Store frequent data in fast memory.

𝗦𝗬𝗦𝗧𝗘𝗠 𝗗𝗘𝗦𝗜𝗚𝗡
→ Distributed Systems: Multiple machines working as one.
→ Message Queue: Buffer for async communication (Kafka, RabbitMQ).
→ Orchestration: Workflow conductor (Airflow, Prefect).
→ Fault Tolerance: Keep running when parts fail.
→ Scalability: Grow without rewriting everything.

💡 Reality Check: Tools change yearly. These concepts? Forever.

Whether you're a DE, analyst, or ML engineer—mastering these puts you ahead of 90% of the field.

Image Credits: Shalini Goyal

Mastering these fundamentals is your true skill. 
Tools change, but the need for reliable, scalable, and governed data pipelines stays forever.
#data #engineering

You opened the Data Engineer job description and saw a full stack of buzzwords: 
→ dbt, SQL, Python, Kafka, Spark, Airflow
→ Docker, Kubernetes, Terraform
→ Snowflake, Databricks, LangChain
→ LLM, RAG, CI/CD
Bonus: Golang/Rust/Scala preferred
5+ years experience ...

✋Relax. That list is a fantasy, not a requirement.

Don't aspire to be perfect, expect more than 50% requirements to meet the job criteria Data Engineers!

They want you to be a Data Scientist + Pipeline Wizard + Cloud Architect + DevOps Engineer + Software Engineer... all in one person. 😅

Here's the truth:
Nobody knows ALL those technologies. Not even the seniors.

What great Data Engineers actually master: 
→ SQL: Non-negotiable mastery of querying, transformations, and modeling.
→ Python: Your go-to for complex scripting, APIs, and pipeline logic.
→ A cloud platform: Deep familiarity with one major provider (AWS, GCP, or Azure).
→ A data warehouse: Expert knowledge of one modern platform (Snowflake, BigQuery, or Databricks).

That’s the foundation. Everything else—the niche tools, the advanced DevOps—is learned 80% on the job.

That's it. That's your foundation.
Everything else? 
— the niche tools, the advanced DevOps—You learn 80% of it ON THE JOB.

Feeling overwhelmed by job descriptions?
That's completely normal. We ALL felt that way starting out.

The real rule: If you meet >50% of the requirements → APPLY.
Don't let a laundry list of buzzwords stop you.

The job reality is far different from the JD. Companies know this. 
They're looking for potential, not perfection.

Image Credits and inspired by - José Siles

Your next role is waiting. Go get it. 💪

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗪𝗮𝗻𝘁𝘀 𝗔𝗜. 𝗡𝗼𝗯𝗼𝗱𝘆 𝗪𝗮𝗻𝘁𝘀 𝘁𝗼 𝗙𝗶𝘅 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮.

The funding pitch that often secures approval includes:
- AI strategy
- GenAI use cases
- Sophisticated dashboards

However, critical elements are frequently overlooked:
- Data definitions
- Quality checks
- Metadata management
- Lineage tracking
- Clear ownership

The truth that data engineers understand is that AI doesn't fail at the algorithm level; it falters because the data it relies on is untrustworthy.

You can't construct a skyscraper on quicksand. While foundational work may not be glamorous, it is essential. It doesn't get showcased to executives or earn innovation accolades, yet it is vital for the stability of everything built above it.

Before pursuing the next AI use case, consider the following:
- Do we have consistent definitions?
- Can we trace the origins of our data?
- Who is responsible for data quality?
- Are our data pipelines tested and monitored?

𝖥𝗂𝗑 𝗍𝗁𝖾 𝖻𝖺𝗌𝖾𝗆𝖾𝗇𝗍 𝖻𝖾𝖿𝗈𝗋𝖾 𝖻𝗎𝗂𝗅𝖽𝗂𝗇𝗀 𝗍𝗁𝖾 𝗉𝖾𝗇𝗍𝗁𝗈𝗎𝗌𝖾.

"𝗔𝗜 𝗶𝘀 𝗼𝗻𝗹𝘆 𝗮𝘀 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗮𝘀 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗶𝘁'𝘀 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗼𝗻. 𝗚𝗮𝗿𝗯𝗮𝗴𝗲 𝗶𝗻, 𝗴𝗮𝗿𝗯𝗮𝗴𝗲 𝗼𝘂𝘁—𝗻𝗼 𝗺𝗮𝘁𝘁𝗲𝗿 𝗵𝗼𝘄 𝗳𝗮𝗻𝗰𝘆 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹."

Illustration inspired by John Wernfeldt

🏗️ Confused by Data Lake vs Warehouse vs Lakehouse vs Mesh?

Selecting the right architecture is game-changing for scalable, trustworthy analytics.

1️⃣ Data Lake
Think of a natural lake — it holds everything: clean water, mud, fish.
Similarly, a Data Lake stores raw data in any format. Flexible, but you’ll need to clean it before use.

2️⃣ Data Warehouse
Imagine a warehouse — everything is neatly packed and labeled.
A Data Warehouse stores structured, cleaned data ready for dashboards and reports. Reliable, but less flexible.

3️⃣ Data Lakehouse
Picture a house by the lake — you get the beauty of the lake and the comfort of a home.
A Data Lakehouse combines raw + structured data. Best of both worlds!

4️⃣ Data Mesh
Think of a network of homes — each family manages its own space.
Data Mesh decentralizes data ownership. Teams treat data as a product and manage it themselves.

🎯 THE SIMPLE TRUTH:
→ Starting out? Use a Data Warehouse (BigQuery, Snowflake, Redshift)
→ Need cheap storage for logs/raw data? Add a Data Lake
→ Want both? Modern Lakehouses (Databricks, Iceberg) are solid
→ Data Mesh? Don't even think about it until you're at massive scale

🎓 Major Takeaways from my experience:
→ Architectures must be aligned with specific project needs for optimal results.
→ Hybrid models like Lakehouse unlock flexibility and performance.
→ Evolving architectures foster innovation and reduce bottlenecks in data pipelines.

Embedding flexible, forward-thinking architecture is vital for data-driven success. 

Companies leading these transformations are able to pace up by 3-5x reducing infrastructure costs by 40-60% through right-sized, purpose-built data systems delivering insights.

Here are some resources to understand more about each of them -
→Designing Data-Intensive Applications by Martin Kleppmann - https://lnkd.in/drm6wQvH
→The Data Warehouse Toolkit by Ralph Kimball - https://lnkd.in/daWQwZ7Q
→Databricks Lakehouse Fundamentals (Free Course) - https://lnkd.in/gQEA-guy
→Google’s Data Mesh Playbook - https://lnkd.in/gumDAdfk

𝘈𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘪𝘴 𝘵𝘩𝘦 𝘣𝘢𝘤𝘬𝘣𝘰𝘯𝘦 𝘰𝘧 𝘪𝘯𝘯𝘰𝘷𝘢𝘵𝘪𝘰𝘯 𝘪𝘯 𝘥𝘢𝘵𝘢 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨.

Success and failure are separated by one decision: 
The choice to keep moving when everything says stop.

Agree?

Data governance is not the security guard saying “no”.

It is more like traffic lights in a busy city – a few rules that let everyone move faster and safely at scale.

Behind every confident decision is invisible governance doing the heavy lifting.
You know that feeling when you pull a report and immediately trust the numbers? 

When you can find the exact data you need in seconds? 
When your dashboard doesn't contradict itself?
That's not luck. That's governance.

🚫 The Misconception vs. ✅ The Reality

What People Think Data Governance Is:
 ❌ The "Department of No" that blocks data access
 ❌ Bureaucratic red tape that kills innovation
 ❌ A 500-page policy document gathering digital dust
 ❌ IT's way of controlling everything

What Data Governance Actually Is:
 ✅ The GPS system for your organization's data
 ✅ The quality control that makes "yes" possible
 ✅ The shared language that stops data arguments
 ✅ The trust layer that turns data into decisions

As a data engineer, you're not building barriers. You're building trust. 
You're not slowing things down. You're preventing the slowdowns that come from:
→ Wrong data in production
→ Compliance violations
→ Metric arguments
→ Security incidents
→ Data disasters

𝘎𝘰𝘰𝘥 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘪𝘴 𝘪𝘯𝘷𝘪𝘴𝘪𝘣𝘭𝘦. 𝘉𝘢𝘥 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘪𝘴 𝘪𝘮𝘱𝘰𝘴𝘴𝘪𝘣𝘭𝘦 𝘵𝘰 𝘪𝘨𝘯𝘰𝘳𝘦.

👉 Here's a quick Reality Check -
💵 Bad governance costs you:
→ 3 AM panic calls
→ Executive trust
→ Real money (hello, compliance fines!)
→ Your sanity

✅ Good governance gets you:
→ Predictable, reliable data
→ Business stakeholder love
→ Career advancement
→ Actual weekends

As a data engineer building governance always ensure that you track:
 • Time to Data - How long until new users can access data they need?
 • Data Trust Score - % of users who trust the data they use
 • Incident Response Time - How fast can you fix data quality issues?
 • Self-Service Adoption - Are users finding data themselves or asking you?
 • Compliance Coverage - % of sensitive data properly classified and protected
 • Metadata Completeness - How well documented is your data?

Here are some incredible folks to follow in the field of Data Governance -
Chad Sanderson
George Firican
🎯 Mark Freeman II
Piotr Czarnas
Dylan Anderson

𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀𝗻'𝘁 𝘁𝗵𝗲 𝗼𝗯𝘀𝘁𝗮𝗰𝗹𝗲 𝘁𝗼 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻—𝗶𝘁'𝘀 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘀𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲.

"I understand Git—I know git commit and git push."
That's like saying "I understand flying—I know takeoff and landing."

Everything in between? That's where pipelines break, data gets lost, and 3 AM incidents happen.

That the biggest myth in Data Engineering!
Even after 8+ years in tech, I've seen teams struggle not with writing code, but with managing it.

Git for Data Engineers: Understand Why It's Different
The Data Engineering Reality:

You're not just versioning code. You're versioning:
→ Pipelines that run on schedules
→ SQL transformations that power dashboards
→ DAGs that orchestrate entire workflows
→ dbt models that define business logic
→ Config files that control production behavior

𝘖𝘯𝘦 𝘸𝘳𝘰𝘯𝘨 𝘮𝘦𝘳𝘨𝘦 = 𝘗𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯 𝘥𝘰𝘸𝘯. 𝘋𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥𝘴 𝘣𝘳𝘰𝘬𝘦𝘯. 𝘊𝘌𝘖 𝘢𝘴𝘬𝘪𝘯𝘨 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴.
Git isn't optional. It's your safety net.

🎓 Git Skills Every Data Engineer Needs

Level 1: Survival 
→ Clone, commit, push, pull
→ Create branches
→ Basic merges

Level 2: Proficiency  
→ Pull requests & code review
→ Resolve merge conflicts
→ Rebase vs merge (know the difference)
→ Cherry-pick commits

Level 3: Mastery 
→ Interactive rebase for clean history
→ Git hooks for automation
→ Submodules for shared code
→ Advanced conflict resolution

Here's a some resources to upskill with Git:
• Learn Git Branching - https://lnkd.in/dvPvYEeC
• Pro Git Book - https://lnkd.in/dasXAAYv

Without Git?
𝗬𝗼𝘂'𝗿𝗲 𝗳𝗹𝘆𝗶𝗻𝗴 𝗯𝗹𝗶𝗻𝗱, 𝗼𝗻𝗲 𝗺𝗶𝘀𝘁𝗮𝗸𝗲 𝗮𝘄𝗮𝘆 𝗳𝗿𝗼𝗺 𝗱𝗶𝘀𝗮𝘀𝘁𝗲𝗿.
With Git?
𝗬𝗼𝘂'𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗹𝗶𝗸𝗲 𝗮 𝗽𝗿𝗼, 𝘄𝗶𝘁𝗵 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗿𝗼𝗹.

Image Credits: Priyanka Vergadia

What's your biggest Git fear or confusion as a data engineer? Let's solve it together in the comments.

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