Struggling with data trust issues, dashboard drama, or constant pipeline firefighting? In this deep‑dive interview, Lior Barak shows you how to shift from a reactive “fix‑it” culture to a mindful, impact‑driven practice rooted in Zen/Wabi‑Sabi principles.

You’ll learn:

Why 97 % of CEOs say they use data, but only 24 % call themselves data‑driven

The traffic‑light dashboard pattern (green / yellow / red) that instantly tells execs whether numbers are safe to use

A practical rule for balancing maintenance, rollout, and innovation—and avoiding team burnout

How to quantify ROI on data products, kill failing legacy systems, and handle ad‑hoc exec requests without derailing roadmaps

Turning “imperfect” data into business value with mindful communication, root‑cause logs, and automated incident review loops

🕒 TIMECODES

00:00 Community and mindful data strategy

04:06 Career journey and product management insights

08:03 Wabi-sabi data and the trust crisis

11:47 AI, data imperfection, and trust challenges

20:05 Trust crisis examples and root cause analysis

25:06 Regaining trust through mindful data management

30:47 Traffic light system and effective communication

37:41 Communication gaps and team workload balance

39:58 Maintenance stress and embracing Zen mindset

49:29 Accepting imperfection and measuring impact

56:19 Legacy systems and managing executive requests

01:00:23 Role guidance and closing reflections

🔗 Connect with Lior

LinkedIn - https://www.linkedin.com/in/liorbarak

Website - https://cookingdata.substack.com/

Cooking Data newsletter: https://cookingdata.substack.com/

Product product lifecycle manager: https://app--data-product-lifecycle-manager-c81b10bb.base44.app/

🔗 Connect with DataTalks.Club

Join the community - https://datatalks.club/slack.html

Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/u/0/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ

Check other upcoming events - https://lu.ma/dtc-events

GitHub: https://github.com/DataTalksClub

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Website - https://datatalks.club/

🔗 Connect with Alexey

Twitter - https://x.com/Al_Grigor

Linkedin - https://www.linkedin.com/in/agrigorev/

In this episode, we talk with Orell about his journey from electrical engineering to freelancing in data engineering. Exploring lessons from startup life, working with messy industrial data, the realities of freelancing, and how to stay up to date with new tools.

Topics covered:

• Why Orel left a PhD and a simulation‑focused start‑up after Covid hit
• What he learned trying (and failing) to commercialise medical‑imaging simulations
• The first freelance project and the long, quiet months that followed
• How he now finds clients, keeps projects small and delivers value quickly
• Typical work he does for industrial companies: parsing messy machine logs, building simple pipelines, adding structure later
• Favorite everyday tools (Python, DuckDB, a bit of C++) and the habit of blocking time for learning
• Advice for anyone thinking about freelancing: cash runway, networking, and focusing on problems rather than “perfect” tech choices

A practical conversation for listeners who are curious about moving from research or permanent roles into freelance data engineering.

🕒 TIMECODES

0:00 Orel’s career and move to freelancing

9:04 Startup experience and data engineering lessons

16:05 Academia vs. startups and starting freelancing

25:33 Early freelancing challenges and networking

34:22 Freelance data engineering and messy industrial data

43:27 Staying practical, learning tools, and growth

50:33 Freelancing challenges and client acquisition

58:37 Tools, problem-solving, and manual work

🔗 CONNECT WITH ORELL

Twitter - https://bsky.app/profile/orgarten.bsk...

LinkedIn - / ogarten

Github - https://github.com/orgarten

Website - https://orellgarten.com

🔗 CONNECT WITH DataTalksClub

Join the community - https://datatalks.club/slack.html

Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/...

Check other upcoming events - https://lu.ma/dtc-events

GitHub: https://github.com/DataTalksClub

LinkedIn - / datatalks-club

Twitter - / datatalksclub

Website - https://datatalks.club/

🔗 CONNECT WITH ALEXEY

Connect with Alexey

Twitter - / al_grigor

Linkedin - / agrigorev

Thinking about swapping your 9‑to‑5 for client work, but worried that a long German–style notice period will kill your chances?  In this live interview, seven‑year data‑freelance veteran Dimitri walks through his experience of taking his freelance career to the next level.

About the Speaker:

Dimitri Visnadi is an independent data consultant with a focus on data strategy. He has been consulting companies leading the marketing data space such as Unilever, Ferrero, Heineken, and Red Bull.

He has lived and worked in 6 countries across Europe in both corporate and startup organizations. He was part of data departments at Hewlett-Packard (HP) and a Google partnered consulting firm where he was working on data products and strategy.

Having received a Masters in Business Analytics with Computer Science from University College London and a Bachelor in Business Administration from John Cabot University, Dimitri still has close ties to academia and holds a mentor position in entrepreneurship at both institutions.

🕒 TIMECODES00:00 Dimitri’s journey from corporate to freelance data specialist05:41 Job tenure trends, tech career shifts, and freelance types10:50 Freelancing challenges, success, and finding clients17:33 Freelance market trends and Dimitri’s job board23:51 Starting points, top freelance skills, and market insights32:48 Building a lifestyle business: scaling and work-life balance45:30 Data Freelancer course and marketing for freelancers48:33 Subscription services and managing client relationships56:47 Pricing models and transitioning advice1:01:02 Notice periods, networking, and risks in freelancing transition

🔗 CONNECT WITH DataTalksClub

Join the community - https://datatalks.club/slack.html

Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/...

Check other upcoming events - https://lu.ma/dtc-events

LinkedIn - / datatalks-club

Twitter - / datatalksclub

Website - https://datatalks.club/

🔗 CONNECT WITH DIMITRI

Linkedin - https://www.linkedin.com/in/visnadi/

In this podcast episode, we talked with Will Russell about From Hackathons to Developer Advocacy.

About the Speaker:

Will Russell is a Developer Advocate at Kestra, known for his videos on workflow orchestration. Previously, Will built open source education programs to help up and coming developers make their first contributions in open source. With a passion for developer education, Will creates technical video content and documentation that makes technologies more approachable for developers.

In this episode, we sit down with Will—developer advocate, content creator, and passionate community builder. We’ll hear about his unique path through tech, the lessons he’s learned, and his approach to making complex topics accessible and engaging. Whether you’re curious about open source, hackathons, or what it’s like to bridge the gap between developers and the broader tech community, this conversation is full of insights and inspiration.

🕒 TIMECODES

0:00 Introduction, career journeys, and video setup and workflow

10:41 From hackathons to open source: Early experiences and learning

16:04 Becoming a hackathon organizer and the value of soft skills

23:18 How to organize a hackathon, memorable projects, and creativity

33:39 Major League Hacking: Building community and scaling student programs

41:16 Mentorship, development environments, and onboarding in open source

49:14 Developer advocacy, content strategy, and video tips

57:16 Will’s current projects and future plans for content creation

🔗 CONNECT WITH DataTalksClub

Join the community - https://datatalks.club/slack.html

Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ

Check other upcoming events - https://lu.ma/dtc-events

LinkedIn - https://www.linkedin.com/company/datatalks-club/

Twitter - https://twitter.com/DataTalksClub

Website - https://datatalks.club/

🔗 CONNECT WITH WILL

LinkedIn - https://www.linkedin.com/in/wrussell1999/

Twitter - https://x.com/wrussell1999

GitHub - https://github.com/wrussell1999

Website - https://wrussell.co.uk/

In this podcast episode, we talked with Lavanya Gupta about Building a Strong Career in Data.

About the Speaker:

Lavanya is a Carnegie Mellon University (CMU) alumni of the Language Technologies Institute (LTI). She works as a Sr. AI/ML Applied Associate at JPMorgan Chase in their specialized Machine Learning Center of Excellence (MLCOE) vertical. Her latest research on long-context evaluation of LLMs was published in EMNLP 2024.

In addition to having a strong industrial research background of 5+ years, she is also an enthusiastic technical speaker. She has delivered talks at events such as Women in Data Science (WiDS) 2021, PyData, Illuminate AI 2021, TensorFlow User Group (TFUG), and MindHack! Summit. She also serves as a reviewer at top-tier NLP conferences (NeurIPS 2024, ICLR 2025, NAACL 2025). Additionally, through her collaborations with various prestigious organizations, like Anita BOrg and Women in Coding and Data Science (WiCDS), she is committed to mentoring aspiring machine learning enthusiasts.

In this episode, we talk about Lavanya Gupta’s journey from software engineer to AI researcher. She shares how hackathons sparked her passion for machine learning, her transition into NLP, and her current work benchmarking large language models in finance. Tune in for practical insights on building a strong data career and navigating the evolving AI landscape.

🕒 TIMECODES

00:00 Lavanya’s journey from software engineer to AI researcher

10:15 Benchmarking long context language models

12:36 Limitations of large context models in real domains

14:54 Handling large documents and publishing research in industry

19:45 Building a data science career: publications, motivation, and mentorship

25:01 Self-learning, hackathons, and networking

33:24 Community work and Kaggle projects

37:32 Mentorship and open-ended guidance

51:28 Building a strong data science portfolio

🔗 CONNECT WITH LAVANYALinkedIn -   / lgupta18  🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/... Check other upcoming events - https://lu.ma/dtc-events LinkedIn -   / datatalks-club   Twitter -   / datatalksclub   Website - https://datatalks.club/

In this podcast episode, we talked with Eddy Zulkifly about From Supply Chain Management to Digital Warehousing and FinOps

About the Speaker:

In this podcast episode, we talked with Bartosz Mikulski about Data Intensive AI.

About the Speaker:

Bartosz is an AI and data engineer. He specializes in moving AI projects from the good-enough-for-a-demo phase to production by building a testing infrastructure and fixing the issues detected by tests. On top of that, he teaches programmers and non-programmers how to use AI. He contributed one chapter to the book 97 Things Every Data Engineer Should Know, and he was a speaker at several conferences, including Data Natives, Berlin Buzzwords, and Global AI Developer Days. 

In this episode, we discuss Bartosz’s career journey, the importance of testing in data pipelines, and how AI tools like ChatGPT and Cursor are transforming development workflows. From prompt engineering to building Chrome extensions with AI, we dive into practical use cases, tools, and insights for anyone working in data-intensive AI projects. Whether you’re a data engineer, AI enthusiast, or just curious about the future of AI in tech, this episode offers valuable takeaways and real-world experiences.

0:00 Introduction to Bartosz and his background

4:00 Bartosz’s career journey from Java development to AI engineering

9:05 The importance of testing in data engineering

11:19 How to create tests for data pipelines

13:14 Tools and approaches for testing data pipelines

17:10 Choosing Spark for data engineering projects

19:05 The connection between data engineering and AI tools

21:39 Use cases of AI in data engineering and MLOps

25:13 Prompt engineering techniques and best practices

31:45 Prompt compression and caching in AI models

33:35 Thoughts on DeepSeek and open-source AI models

35:54 Using AI for lead classification and LinkedIn automation

41:04 Building Chrome extensions with AI integration

43:51 Comparing Cursor and GitHub Copilot for coding

47:11 Using ChatGPT and Perplexity for AI-assisted tasks

52:09 Hosting static websites and using AI for development

54:27 How blogging helps attract clients and share knowledge

58:15 Using AI to assist with writing and content creation

🔗 CONNECT WITH Bartosz

LinkedIn: https://www.linkedin.com/in/mikulskibartosz/

Github: https://github.com/mikulskibartosz

Website: https://mikulskibartosz.name/blog/

🔗 CONNECT WITH DataTalksClub

Join the community - https://datatalks.club/slack.html

Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ

Check other upcoming events - https://lu.ma/dtc-events

LinkedIn - https://www.linkedin.com/company/datatalks-club/

Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/

In this podcast episode, we talked with Nemanja Radojkovic about MLOps in Corporations and Startups.

About the Speaker:

Nemanja Radojkovic is Senior Machine Learning Engineer at Euroclear.

In this event,we’re diving into the world of MLOps, comparing life in startups versus big corporations. Joining us again is Nemanja, a seasoned machine learning engineer with experience spanning Fortune 500 companies and agile startups. We explore the challenges of scaling MLOps on a shoestring budget, the trade-offs between corporate stability and startup agility, and practical advice for engineers deciding between these two career paths. Whether you’re navigating legacy frameworks or experimenting with cutting-edge tools.

1:00 MLOps in corporations versus startups

6:03 The agility and pace of startups

7:54 MLOps on a shoestring budget

12:54 Cloud solutions for startups

15:06 Challenges of cloud complexity versus on-premise

19:19 Selecting tools and avoiding vendor lock-in

22:22 Choosing between a startup and a corporation

27:30 Flexibility and risks in startups

29:37 Bureaucracy and processes in corporations

33:17 The role of frameworks in corporations

34:32 Advantages of large teams in corporations

40:01 Challenges of technical debt in startups

43:12 Career advice for junior data scientists

44:10 Tools and frameworks for MLOps projects

49:00 Balancing new and old technologies in skill development

55:43 Data engineering challenges and reliability in LLMs

57:09 On-premise vs. cloud solutions in data-sensitive industries

59:29 Alternatives like Dask for distributed systems

🔗 CONNECT WITH NEMANJA

LinkedIn -   / radojkovic  

Github - https://github.com/baskervilski

🔗 CONNECT WITH DataTalksClub

Join the community - https://datatalks.club/slack.html

Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/...

Check other upcoming events - https://lu.ma/dtc-events 

LinkedIn -   / datatalks-club   

Twitter -   / datatalksclub   

Website - https://datatalks.club/ 

In this podcast episode, we talked with Adrian Brudaru about ​the past, present and future of data engineering.

About the speaker:

In this podcast episode, we talked with Alexander Guschin about launching a career off Kaggle.

About the Speaker: