Mike Koziol

I develop strategic plans that help reach company goals and targets and improve profitability

Business Strategist

ComplianceIQ

Strategic Compliance Consulting. Fraud investigations. Corporate governance. Compliance monitoring services.

Founder

University Canada West

MBA program faculty member  Department of Marketing, Strategy and Entrepreneurship.

MBA Faculty Lecturer -  Business Strategy, Business Ethics, CSR

Eight Loop Social

A business strategy coach helping marketing agency owners grow their businesses, achieve efficiency and increase revenue. Consulted on:
- effective digital marketing strategies to get more clients
- team structure
- growth strategies

Coach

Teva Canada

Head Of Compliance Canada, Generics

Bizness Rebels

Co Founder

Johnson & Johnson

Reviewing and assessing if the requests to conduct activities made by JnJ employees are
in compliance with applicable Regulations; as a result – making approval or refusal decision 
with regards to these activities
- Conducting or initiating internal investigations concerning potential violations of company policy or the law
- Supporting JnJ in managing delicate ethical issues, conducting explanatory proceedings, investigations, monitoring, and audits
- Compliance and legal documentation assessment
- Preventing and detecting conduct inconsistent with JnJ policies
- Assisting in the development and oversight of a third party compliance auditing and monitoring program

External Compliance Adviser. Compliance Monitoring.

Valeant Pharmaceuticals

Supporting 4 Business Units (300+ employees);
New compliance framework and approval process setup;
Review of sales & marketing activities;
Adjusting procurement process;
Implementing post-audit recommendations;
Annual certifications & Trainings;
Business analysis;
Projects implementation

Country Head of Compliance

Novartis

Fixed time project (interim):
Verification of marketing activities;
Design and implementation of compliance responsibilities framework;
Introducing new compliance approval processes;
Post audit recommendations implementation;
Internal communication;

Head of Compliance (Interim Position)

Deloitte

Conducted 6 projects:
Compliance advisory;
Compliance audits and reviews;
Internal audit support;
Business consultancy (processes effectiveness improvement);
KYC / AML;
Healthcare Compliance;
Project implementation;
UKBA, FCPA;
Financial institutions, healthcare, telco

Senior Compliance Consultant

Feeling stuck? I help founders earn more while working less. | Helped 900+ Solo & Entrepreneurs Gain Freedom | 📈 6-Figure 86% Profit Margin Biz 💵 | Daily posts on how

Transform Your Agency into a Scalable, Profit-Generating Machine

Are you an agency owner feeling stuck in the grind, overwhelmed by client demands, and struggling to scale your business? Imagine transforming your agency into a powerhouse that consistently generates $100k+ in monthly revenue, with 30%+ profit margins, all while freeing up 50% of your time for strategic growth or personal freedom.

That’s not just a dream—it’s a reality I help my clients achieve.

With over 15 years of experience, including successfully running my own two marketing agencies, I’ve seen firsthand the challenges of scaling a business. I’ve taken those hard-earned lessons and developed a proven framework that has helped over 900+ businesses level up and reach new heights.

Here’s how I can help you:

Revenue Growth: I’ll work with you to implement strategies that drive your agency’s revenue to $100k+ per month within 12 months.

Profit Maximization: We’ll focus on increasing your profit margins to 30%+ by optimizing your service delivery and cutting unnecessary costs.

Time Freedom: By streamlining operations and introducing productized services, I’ll help you reclaim 50% of your time, allowing you to focus on what truly matters—whether that’s scaling your business further or enjoying more personal freedom.

My Approach:

Productized Services: I’ll teach you how to streamline client delivery with productized services, making your agency more efficient and scalable.

Strategic Business Model Upgrades: I’ll help you shift your business model to one that pays you what you’re worth while giving you more time and less stress.

Operational Streamlining: My framework simplifies and automates key processes, so you can focus on growth, not the grind.

Why Work with Me?

Proven Results: My clients have consistently achieved $100k+ in monthly revenue, with many surpassing these goals, while maintaining healthy profit margins and enjoying more personal freedom.

Experience You Can Trust: I’ve been in the trenches—first as an agency owner, and now as a solopreneur consultant. I understand the challenges you’re facing and know how to overcome them.

No Fluff, Just Results: I’m committed to providing actionable strategies that get you results, without the unnecessary jargon or fluff.

If you’re ready to transform your agency into a revenue-generating machine that doesn’t just survive but thrives, let’s connect.

Work with me: https://mikekoziol.com/connect/
Pre-recorded courses: https://mikekoziol.com/
Newsletter: https://mikekoziol.com/consulting-news/

🏄‍♂️ Dmitry Anoshin

Surfalytics

Surfalytics = Surfing + Analytics. Inspired by West Coast surfing spots and Pacific ocean vibes. Created with goal to help you start a new career in data analytics space, develop data engineering and analytics skills through coaching. It will teach you not just dry skills, but will keep your focus to deliver significant value to businesses in the analytics realm as well as help to get fair compensation for the work you're passionate about.

There are several key producs:

1. Membership in a community with the goal of landing the job, preparing for the interview and getting the best offer. Every month, we do a Data Engineering and Analytics project and discuss our progress on the journey.

2. Community support to
2.1. Land 1st job in data
2.2. Move from analyst to engineer
2.3. Promote from middle to senior and beyond

3. Free course based on my 14 years of experience in Data Engineering and Data Analytics

Data Engineering and Analytics Mentor

University of Victoria

Instructor | Cloud Computing, Analytics and Data Engineering

Rock Your Data

- Data Team building and solving bottlenecks in team delivery
- Building Data Engineering Solutions with Modern Data Stack
- Data Infrastructure and Data Ops (DevOps)
- Data Security and Compliance

Advisory Board Member

Microsoft

- Building Delta Lake on top Databricks and Azure for Microsoft Gaming
- Structured Streaming
- Self-Service BI

Data Engineering & ML

Amazon

- Designed and Implemented Cloud Data Platform for Alexa Quality Metrics
- Designed and Implemented ML platform into production for Customer Perception, Onsite Experience and Churn
- Analytics Modernization with AWS
- Data privacy and Security
- Tracked and Optimized solution cost

Locations: Vancouver, Cambridge, Seattle

Communities:
- Lead Amazon Tableau User Group with 2k+ users.
- Lead Amazon BI Tech Talk community with 100+ BI teams globally.

Packt

•	Author of "Azure Data Factory Cookbook" - Build and manage ETL and ELT pipelines with Microsoft Azure's serverless data integration service. Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data Factory.
•	Author of "Tableau Desktop Certified Associate: Exam Guide" - This book helps to develop your Tableau skills and prepare for Tableau certification with tips from industry experts.

•	Author of "Tableau Cookbook 2019.x" - This book has over 600 pages and includes over 115 recipes that help you to master Tableau Desktop, Prep, and Server.
•	Author of "Mastering Business Intelligence with Microstrategy" - This book is intended to master the craft of Business Intelligence using the best BI platform and discover the full potential of Microstrategy 10 in order to increase performance and improve the user experience.

•	Author of "SAP Lumira Essentials" - Learn data discovery techniques, build amazing visualizations, create fantastic stories and share these visualizations through an electronic medium with one of the most powerful tools – SAP Lumira. 

•	Author of "Learning Hunk"​ - Explore, analyze and visualize Big Data in Hadoop or NoSQL data stores with powerful full-featured big data analytics platform Hunk. 

•	Reviewer of "Learning Tableau 2019"​
•	Reviewer of "Mastering Tableau 2019.1"​
•	Reviewer of "SAP BusinessObjects Reporting Cookbook"​
•	Reviewer of "Creating Universes with SAP Business Objects"​
•	Reviewer of "Learning SAP Business Objects Dashboards"
•	Reviewer of "Splunk 7 Essentials 3rd Edition"

Author & Technical Reviewer

Apress

Jumpstart Snowflake A Step-by-Step Guide to Modern Cloud Analytics. This book is about modern analytics in cloud data warehouse age. It covers cloud analytics fundamentals and highlights modern analytics requirements. The book helps you to start with Snowflake and explore unique features and architecture. and learn more about the modern analytics ecosystem. You will review the analytics solution architectures and migration/modernization scenarios.

Author

Wawanesa Insurance

Toolbox: Tableau, Microstrategy, Oracle, Erwin Data Modeller, Informatica PowerCenter

Data Engineering & BI

Forex Club

DI toolbox: Tableau, Amazon Redshift, Postgresql, Pentaho DI, Tealium, DMP, Klipfolio, Adobe SiteCatalyst, Google Analytics, Adjust

Data Engineer

Teradata

• Delivered technical concepts, designed & developed of BI/ETL/DWH, Splunk, Web Analytics, Campaign Management, Data Science, BigData Analytics
• Designed and Implemented BI architecture and developed the full life cycle of the Business Intelligence part of the corporate BI solution for 1,000+ end users

Data Engineering Leader with 14 years of experience working in Business Intelligence, Data Warehouse & Data Integration, BigData, Cloud, and ML space across North America and Europe.

Leading Data Engineering initiatives, working on a petabyte size data platform built using cloud and big data technologies for supporting machine learning experiments, data science models, business intelligence reporting, and data exchange with internal and external partners. Handling privacy compliance and security-critical data sets.

Apart from work, I am teaching a Cloud Computing course at the University of Victoria, mentoring high school students at CS faculty, and volunteering my time for coaching people with analytics engineering skills in the CIS region. Moreover, I am the author of analytics books and speaker at data related conferences and user groups.

Data Engineering and Analytics Chief Instructor

Surfalytics = Surfing + Analytics. Inspired by West Coast surfing spots and Pacific ocean vibes. Created with goal to help you start a new career in data analytics space, develop data engineering and analytics skills through coaching. It will teach you not just dry skills, but will keep your focus to deliver significant value to businesses in the analytics realm as well as help to get fair compensation for the work you're passionate about.

There are several key producs:

1. Membership in community with the goal to land the job, prepare for the interview and get best offer. Every Saturday we do a Data Engineering and Analytics projects and discuss our progress on the journey.

2. Career consultations from Dmitry with focus on
2.1. Land 1st job in data
2.2. Move from analyst to engineer
2.3. Promote from middle to senior and beyond

3. Free course based on my 14 years experience in Data Engineering and Data Analytics

"𝐉𝐮𝐬𝐭 𝐮𝐬𝐞 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐬 𝐚𝐬 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚 𝐰𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞"

Until you need:
- 5TB+ of data
- Complex joins over billions of rows
- Sub-second dashboard queries for 300+ users
- Time travel, cloning, and zero-copy environments
- Elastic compute separation for ETL vs BI

Then you’re not using Postgres.

You’re building an MPP cloud data platform.

Most engineers think “Postgres as a warehouse” =
- Create a cluster
- Add indexes
- Partition big tables
- Throw more CPU/RAM
- Done

Reality hits around 1–2TB and 200M+ rows.

You’re no longer running analytics on Postgres.

You’re fighting Postgres.

What scaling actually means:
- Query queueing & concurrency management
- Columnar storage for analytical workloads
- Elastic scaling for transformation pipelines
- Automatic clustering & micro-partition pruning
- Separation of compute & storage

Your <1s dashboard breaks down like:
- Network: 30–50ms
- Data fetch: 100–200ms
- Join across 800M rows: 300–600ms
- Aggregations: 200–400ms

You have 0–200ms buffer.
"Just add indexes" doesn't work.

Storage is the silent killer.
- 3TB in Postgres x 3 replicas x WAL overhead = 10+ TB real usage
- Vacuuming & reindexing = hours of block time
- Backups restore time = multi-hour downtime

Contrast with Snowflake / BigQuery / Databricks:
- Storage = pennies
- Compression = 4–10x
- Zero-copy clones = instant
- Auto-scaling warehouses

“We need to run transformations while dashboards are live”

Now your platform needs:
- Separated compute clusters
- Task orchestration & workload isolation
- Query optimization with statistics
- Automatic cluster suspend/resume
- Materialized views that actually work

This is where 80% of Postgres-as-DW projects die.

When Postgres is fine:
- Store up to 300–500GB
- Run BI on 10–20 users
- Rarely need heavy joins
- Can tolerate 3–5s latency
- ETL runs once per day

When you move to MPP:
- Switch to Snowflake / BigQuery / Databricks / Redshift / ClickHouse when:
- 2TB+ data
- 50–500 dashboard users
- Real-time ELT
- Sub-second latency requirements
- Multiple compute workloads

Cost math:
- Postgres cluster 64 vCPU / 256GB RAM on cloud: ~$2,500/mo + ops & tuning
- Snowflake / BigQuery on demand compute: ~$800–1500/mo for same workload

But add $100K/year engineering & admin cost.

8 months later — MPP pays for itself.

Production data platforms have 4 layers:
- Query planner & optimizer
- Distributed SQL execution engine
- Storage engine with micro-partitions & pruning
- Observability, caching, workload isolation


Postgres is brilliant.
It’s not a data warehouse.

At scale, it becomes technical debt.

Staff AI Data Engineer | Data Infrastructure for AI | Data Engineering & Analytics System Design Community | 8x tech book author

I recently read “5 𝐓𝐡𝐢𝐧𝐠𝐬 𝐈𝐧 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐓𝐡𝐚𝐭 𝐇𝐚𝐯𝐞 𝐂𝐡𝐚𝐧𝐠𝐞𝐝 𝐈𝐧 𝐓𝐡𝐞 𝐋𝐚𝐬𝐭 10 𝐘𝐞𝐚𝐫𝐬” by Seattle Data Guy — and it strongly resonated with my experience working across North America and globally. 

Here are the points that stood out to me the most, with my personal perspective👇

1) 𝘌𝘷𝘦𝘳𝘺𝘰𝘯𝘦 𝘞𝘢𝘯𝘵𝘴 𝘚𝘦𝘯𝘪𝘰𝘳𝘴
𝘛𝘦𝘢𝘮𝘴 𝘢𝘳𝘦 𝘮𝘶𝘤𝘩 𝘴𝘮𝘢𝘭𝘭𝘦𝘳 𝘯𝘰𝘸, 𝘦𝘹𝘱𝘦𝘤𝘵𝘢𝘵𝘪𝘰𝘯𝘴 𝘢𝘳𝘦 𝘩𝘪𝘨𝘩𝘦𝘳, 𝘢𝘯𝘥 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴𝘦𝘴 𝘸𝘢𝘯𝘵 𝘪𝘮𝘱𝘢𝘤𝘵 𝘧𝘢𝘴𝘵 - 𝘴𝘰 𝘵𝘩𝘦𝘺 𝘩𝘪𝘳𝘦 𝘰𝘯𝘭𝘺 𝘴𝘦𝘯𝘪𝘰𝘳 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘴 𝘸𝘩𝘰 𝘤𝘢𝘯 𝘥𝘦𝘭𝘪𝘷𝘦𝘳 𝘧𝘳𝘰𝘮 𝘥𝘢𝘺 𝘰𝘯𝘦. 

Entering the industry as a junior has become extremely difficult. Companies rarely want to invest in training. If you’re not from a top-tier university, the path is even tougher. And the course-to-job pipeline is mostly broken.

That’s why the strategy “enter as a senior without traditional experience” is becoming extremely attractive — build strong portfolio projects, real results, and business value instead of certificates.

2) 𝘊𝘭𝘰𝘶𝘥 𝘉𝘦𝘤𝘢𝘮𝘦 𝘵𝘩𝘦 𝘋𝘦𝘧𝘢𝘶𝘭𝘵
10 𝘺𝘦𝘢𝘳𝘴 𝘢𝘨𝘰 𝘤𝘭𝘰𝘶𝘥 𝘸𝘢𝘴 𝘢𝘯 𝘰𝘱𝘵𝘪𝘰𝘯.
𝘛𝘰𝘥𝘢𝘺 𝘪𝘵’𝘴 𝘢 𝘳𝘦𝘲𝘶𝘪𝘳𝘦𝘮𝘦𝘯𝘵.

AWS / Snowflake / BigQuery / Databricks / GCP — you’ll find them in 95% of job descriptions globally. On-prem still exists only for regulation or very specific cost cases.

3) 𝘕𝘰 𝘖𝘯𝘦 𝘉𝘶𝘪𝘭𝘥𝘴 𝘊𝘶𝘴𝘵𝘰𝘮 𝘗𝘪𝘱𝘦𝘭𝘪𝘯𝘦𝘴 𝘈𝘯𝘺𝘮𝘰𝘳𝘦
𝘈 𝘥𝘦𝘤𝘢𝘥𝘦 𝘢𝘨𝘰 𝘌𝘛𝘓 𝘸𝘢𝘴 𝘢𝘭𝘭 𝘤𝘳𝘰𝘯 𝘫𝘰𝘣𝘴, 𝘚𝘚𝘐𝘚, 𝘗𝘺𝘵𝘩𝘰𝘯 𝘴𝘤𝘳𝘪𝘱𝘵𝘴 𝘢𝘯𝘥 𝘪𝘯𝘵𝘦𝘳𝘯𝘢𝘭 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬𝘴.

Low-code / no-code tools and AI automation allow engineers to focus on business impact instead of debugging legacy pipelines. Technical fundamentals still matter - but reinventing the wheel doesn’t.

4) 𝘚𝘘𝘓 𝘞𝘰𝘯

If your data product doesn’t support SQL, it’s a bad product. Period.
Yes, some analysts love living inside Pandas notebooks, but eventually, engineers need to productionize and clean it all up. 😅

5) 𝘈𝘐 𝘊𝘩𝘢𝘯𝘨𝘦𝘥 𝘌𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨
𝘈𝘐 𝘢𝘤𝘤𝘦𝘭𝘦𝘳𝘢𝘵𝘦𝘴 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨, 𝘳𝘦𝘥𝘶𝘤𝘦𝘴 𝘳𝘦𝘱𝘦𝘵𝘪𝘵𝘪𝘷𝘦 𝘸𝘰𝘳𝘬 𝘢𝘯𝘥 𝘮𝘢𝘬𝘦𝘴 𝘴𝘪𝘯𝘨𝘭𝘦 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘦𝘳𝘴 𝘴𝘪𝘨𝘯𝘪𝘧𝘪𝘤𝘢𝘯𝘵𝘭𝘺 𝘮𝘰𝘳𝘦 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘷𝘦.

But it also creates a risk: people rely on LLMs without understanding what they are doing. They copy code instead of thinking. Movement ≠ progress.
The winners are those who combine AI with deep engineering fundamentals and create real business value.

For me, the biggest shift in 10 years isn’t even tech — it’s economics:
->lower purchasing power
->higher taxes and cost of living
->disappearing job security
->no stability

Everyone wants a high salary and remote work, but there won’t be enough seats for everyone.

The next decade will belong to:
🔥 engineers who think like owners
🔥 experts who move fast and deliver value
🔥 those who can adapt, learn and use AI as leverage

Shubham Srivastava has a great list of data engineering things that are great to have. But if you want just to start and get your first job, you can focus on building one end-to-end project with low/no code:

1. Pick a public API (for example, GitHub)
2. Use Firvetran to extract data (free up to 500k rows per month)
3. Use Snowflake to ingest data (free trial up to 6 months with help of Coursera)
4. Use dbt core (free forever) to transform data with SQL to answer business questions (make up these questions)
5. Use GitHub to store the code and schedule dbt
6. Connect open source BI like Metabase or a free trial of Looker/Tableau
7. Add Airflow if you want a fully end-to-end

Advance topics
8. Replace Fivtran with Airflow and Python
9. Add AWS resources
10. Use Terraform to create resources in Snowflake and AWS
11. Add tests with dbt and soda

Master these topics and mock the system design interview, and you will be fine.

Principal Data Engineer @ Amazon | Data Engineering

Deciding to get into Data Engineering is one long chain of decisions:

- decide you want to be a data engineer
- realize you need to learn SQL first
- spend three weeks on window functions
- pick Python or Scala
- pick Python
- learn pandas
- rewrite everything in polars
- choose a cloud platform
- second-guess that platform
- set up AWS (then get lost in IAM hell)
- learn Docker
- debug Docker networking
- add docker-compose because fuck it
- realize you need Kubernetes
- decide Kubernetes is overkill
- pick an orchestrator: Airflow vs Prefect vs Dagster
- install Airflow locally
- spend two days fixing dependencies
- learn about DAGs
- write your first pipeline
- it fails silently
- add logging everywhere
- learn about idempotency (probably too late)
- set up a data warehouse
- choose between Snowflake, BigQuery, Redshift
- immediately regret your choice
- design your first star schema
- refactor it into a snowflake schema
- go back to star schema
- learn dbt
- rewrite all your transforms in dbt
- add tests (after breaking prod)
- handle incremental loads
- forget about deduplication
- add partitioning
- add clustering
- learn about slowly changing dimensions (ugh…)
- set up CI/CD
- break it
- fix it, add data quality checks
- learn Spark because the data got big
- debug Spark memory issues
- optimize Spark jobs you don't understand
- add monitoring and alerting
- get paged at 2am for a failed pipeline
- learn about data contracts
- write documentation nobody reads
- build a data catalog
- update your resume
- apply to jobs
- get rejected for not knowing tool X
- learn tool X
- that tool is now deprecated
- start a portfolio project
- abandon it halfway
- start another one
- ship it
- immediately find a data quality issue

it's the learning path for probably 90% of data engineers who entered the field in the last 5-10 years, and you're doing all this while trying to stay relevant in a field that changes every 6 months.

📖 Just finished "Empire of AI" by Karen Hao - and it made me realize: AI isn't just a story of efficiency and innovation. It's also a story of hidden trade-offs - in labour, resources, and power.

The uncomfortable truth? Building AI requires:
-> Data "cleaned" by workers earning <$1/day in Kenya
-> Data centers consume as much energy as entire cities
-> The creative work of millions, scraped without consent

Hao calls it what it is: a new age of empire.
Whether you're bullish or skeptical on AI, this is essential reading.

taplio

The Battle

Mike Koziol

🏄‍♂️ Dmitry Anoshin

Followers

Posts

Engagements

Likes

Performance Score🏄‍♂️ Dmitry Anoshin's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Mike Koziol

🏄‍♂️ Dmitry Anoshin

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}🏄‍♂️ Dmitry Anoshin's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance Score🏄‍♂️ Dmitry Anoshin's LinkedIn Performance