Updated titles, intros

Author: kavaivaleri

_podcast/s03e04-interviewing-300-data-scientists.md

@@ -1,8 +1,7 @@
 ---
 title: 'Data Science Interview Guide: CV Optimization, Take-Home Projects, Mock Interviews
   & Negotiation'
-short: 'Data Science Interview Guide: CV Optimization, Take-Home Projects, Mock Interviews
-  & Negotiation'
+short: 'Data Science Interview Guide: CV Optimization, Take-Home Projects, Mock Interviews & Negotiation'
 guests:
 - olegnovikov
 image: images/podcast/s03e04-interviewing-300-data-scientists.jpg
@@ -919,23 +918,23 @@ transcript:
   sec: 4194
   time: '1:09:54'
   who: Alexey
-intro: How do you make your data science application stand out, ace take-home projects,
-  and negotiate an offer without leaving money on the table? In this episode Oleg
+intro: "How do you make your data science application stand out, ace take-home projects,
+  and negotiate an offer without leaving money on the table? In this episode, Oleg
   Novikov — creator of NextRound and former data science manager at Uber with a background
-  in data and software engineering — walks through a practical Data Science interview
+  in data and software engineering — walks through a practical data science interview
   guide covering CV optimization, take-home projects, mock interviews, and negotiation.
   <br><br> We dig into career trajectory from engineering to product data science,
   building projects that differentiate your application, and concrete product work
-  like forecasting and LTV. Oleg demos NextRound’s mock-interview chatbot and personalized
+  like forecasting and LTV. Oleg demonstrates NextRound's mock-interview chatbot and personalized
   feedback, explains common hiring funnels (recruiter screen → take-home → interviews),
   and contrasts product data scientist vs. machine learning engineer expectations.
-  You’ll hear specific advice on treating your CV as a landing page, highlighting
+  You'll hear specific advice on treating your CV as a landing page, highlighting
   personal contributions, crafting case-study narratives from business goals to evaluation
   metrics, and preparing for technical assessments (ML fundamentals, SQL window functions,
   coding). We also cover handling rejection, replying graciously, evaluating offers,
   negotiation tactics when your current salary is low, and practical steps for PhDs
   breaking into industry. <br><br> Listen for actionable steps to refine your data
-  science resume, prioritize take-home ROI, and use mock interviews to iterate faster.
+  science resume, prioritize take-home ROI, and use mock interviews to iterate faster."
 ---
 Links:
 

_podcast/s03e07-market-yourself.md

@@ -1011,21 +1011,21 @@ transcript:
   sec: 3830
   time: '1:03:50'
   who: Alexey
-intro: 'How do developers build visibility, earn promotions, and steer their careers
-  by “learning in public”? In this episode Shawn “swyx” Wang — Senior Developer Advocate
+intro: "How do developers build visibility, earn promotions, and steer their careers
+  by learning in public? In this episode, Shawn Swyx Wang — Senior Developer Advocate
   for AWS Amplify, author of The Coding Career Handbook, and former engineer at Netlify
   and Temporal — walks through a practical framework for personal branding and career
-  marketing for developers. We unpack why self‑marketing matters beyond job hunting,
-  and the five‑part personal marketing framework: brand, domain, value, skills, and
-  channel. <br><br> You’ll hear concrete guidance on choosing and validating a niche
+  marketing for developers. We unpack why self-marketing matters beyond job hunting
+  and the five-part personal marketing framework: brand, domain, value, skills, and
+  channel. <br><br> You'll hear concrete guidance on choosing and validating a niche
   (meetups, conferences, community signals), building an owned platform (blog, newsletter,
   mailing list), and distribution tactics from early social growth to the engagement
-  move “pick up what they put down.” Swyx also covers career transition strategies,
+  move \"pick up what they put down.\" Swyx also covers career transition strategies,
   hiring portfolios and case studies, internal pathways like lateral moves and signature
   initiatives, and creating reusable talks and demos. Practical tools discussed include
   brag documents, demos for internal promotion, and open knowledge projects as visibility
   builders. Tune in to get actionable steps to craft a developer personal brand, grow
-  influence, and apply learn‑in‑public tactics to advance your career and job opportunities.'
+  influence, and apply learn-in-public tactics to advance your career and job opportunities."
 ---
 Links:
 

_podcast/s04e08-freelancing.md

@@ -1136,21 +1136,21 @@ transcript:
   sec: 3827
   time: '1:03:47'
   who: Alexey
-intro: 'How do you build a reliable freelance career around machine learning when
-  clients expect production-ready systems, not just prototypes? In this episode Mikio
+intro: "How do you build a reliable freelance career around machine learning when
+  clients expect production-ready systems, not just prototypes? In this episode, Mikio
   Braun — an ML researcher who has moved models into production at European unicorns
   Zalando and GetYourGuide and now advises companies as a consultant — walks through
   what freelancing in machine learning really involves. <br><br> We focus on practical,
   end-to-end concerns: aligning ML work with product goals, designing ML infrastructure
   that supports deployment and maintenance, and translating research or proofs-of-concept
-  into production-grade solutions. Mikio’s background in both research and industry
-  gives him direct experience with the technical and product-side tradeoffs that matter
+  into production-grade solutions. Mikio's background in both research and industry
+  gives him direct experience with the technical and product-side trade-offs that matter
   to clients hiring an ML consultant or machine learning freelancer. <br><br> Listeners
   will come away with concrete perspectives on where freelance ML work adds value,
   how to scope engagements that bridge experimentation and production, and what to
   prioritize when building ML systems for real users. This episode is essential for
   machine learning freelancers, aspiring ML consultants, and product teams evaluating
-  external ML expertise.'
+  external ML expertise."
 ---
 
 Books: 

_podcast/s05e02-data-engineering-acronyms.md

@@ -1022,21 +1022,21 @@ transcript:
   sec: 3689
   time: '1:01:29'
   who: Alexey
-intro: How do you decide between ETL and ELT, or when to keep a data lake versus a
+intro: "How do you decide between ETL and ELT, or when to keep a data lake versus a
   warehouse—and where do tools like Airbyte, dbt, and CDC fit into a modern data stack?
-  In this episode Natalie Kwong, Growth Product Manager at Airbyte with prior analytics
-  and ops roles at Harness, KeepTruckin, and AppDynamics, pulls from hands‑on experience
+  In this episode, Natalie Kwong, Growth Product Manager at Airbyte with prior analytics
+  and ops roles at Harness, KeepTruckin, and AppDynamics, pulls from hands-on experience
   scaling analytics teams and systems to unpack these trade-offs. <br><br> We break
   down core concepts—ETL (traditional extract-transform-load) vs ELT (load then transform),
   the rise of the analytics engineer, and why ELT favors analyst autonomy with dbt.
-  Natalie explains Airbyte’s role as a connector/ingestion layer, CDC for row‑level
+  Natalie explains Airbyte's role as a connector/ingestion layer, CDC for row-level
   change syncing, and orchestration with Airflow. We also cover data lake vs data
   warehouse purposes, preventing data swamps through governance, schema evolution,
   operational reverse data flows, and when hybrid architectures make sense. <br><br>
-  If you’re designing a modern data platform or refining pipelines, this episode offers
+  If you're designing a modern data platform or refining pipelines, this episode offers
   practical guidance on ETL vs ELT decisions, choosing lakes vs warehouses, leveraging
   Airbyte and dbt, and operational considerations like data quality, orchestration,
-  and cleanup practices.
+  and cleanup practices."
 ---
 
 Links:

_podcast/s06e01-solopreneur.md

@@ -1033,19 +1033,19 @@ transcript:
   sec: 3610
   time: '1:00:10'
   who: Alexey
-intro: How do you build a sustainable solopreneur business that doesn’t rely on VC
-  funding—while diversifying income across courses, consulting, books and side‑gigs?
-  In this episode Noah Gift, founder of Pragmatic AI Labs and a lecturer on machine
+intro: "How do you build a sustainable solopreneur business that doesn't rely on VC
+  funding—while diversifying income across courses, consulting, books, and side-gigs?
+  In this episode, Noah Gift, founder of Pragmatic AI Labs and a lecturer on machine
   learning and data science at Northwestern, Duke MIDS, UC Berkeley, UC Davis, and
   UNC Charlotte, walks through his transition to solo work (since 2017) and a repeatable
-  income mix for intentional small‑business ownership. <br><br> We cover defining
-  solopreneurship, the practical “income mix” formula—online courses, university teaching,
-  selective consulting, book publishing, apps, real estate and investments—plus how
-  to build a side‑gig “tunnel” while employed. Noah shares work allocation strategies
-  (exponential projects vs. consulting), publishing tradeoffs, a book workflow (outline
-  → projects → write), daily routines, time‑and‑cost tactics, and signals for financial
-  readiness to quit full‑time work. If you’re planning to diversify income streams
-  with online courses, consulting or writing, this episode gives actionable steps,
-  publishing considerations, and networking advice to help you transition deliberately
-  and scale revenue without sacrificing control.
+  income mix for intentional small-business ownership. <br><br> We cover defining
+  solopreneurship, the practical income mix formula (online courses, university teaching,
+  selective consulting, book publishing, apps, real estate, and investments) plus how
+  to build a side-gig tunnel while employed. Noah shares work allocation strategies
+  (exponential projects vs. consulting), publishing trade-offs, a book workflow (outline
+  -> projects -> write), daily routines, time-and-cost tactics, and signals for financial
+  readiness to quit full-time work. <br><br> If you're planning to diversify income
+  streams with online courses, consulting, or writing, this episode gives actionable
+  steps, publishing considerations, and networking advice to help you transition deliberately
+  and scale revenue without sacrificing control."
 ---

_podcast/s07e05-machine-learning-system-design-interview.md

@@ -2,22 +2,22 @@
 episode: 5
 guests:
 - valeriybabushkin
-intro: 'How do you approach ML system design interviews that probe production constraints,
-  fraud detection trade-offs, and MLOps realities? In this episode Valerii Babushkin
+intro: "How do you approach ML system design interviews that probe production constraints,
+  fraud detection trade-offs, and MLOps realities? In this episode, Valerii Babushkin
   — Senior Director of Data, Analytics, and AI at BP, Kaggle Competitions Grandmaster,
   and author of Machine Learning System Design — walks through what interviewers look
   for and how candidates should structure answers for real-world ML problems. <br><br>
   We cover concrete topics you can use in interviews and on the job: distinguishing
-  software vs ML system design, a fraud detection case study (probabilities, loss
-  functions, real-time requirements), label noise, class imbalance, and feature engineering
+  software vs. ML system design; a fraud detection case study (probabilities, loss
+  functions, real-time requirements); label noise, class imbalance, and feature engineering
   trade-offs; end-to-end pipeline items like metrics, baselines, A/B testing, and
   validating in production; monitoring, distribution shift, fallbacks, and production
   robustness; serving models, embeddings, and MLOps roles; plus when to avoid ML and
   practical checklist items for core projects. Valerii also shares interview tactics
   — signposting depth, stating assumptions, iterative baselines — and guidance for
   new grads and career progression toward system design roles. <br><br> Listen to
   learn actionable frameworks, example trade-offs, and preparation strategies to improve
-  your ML system design interviews and production ML decisions.'
+  your ML system design interviews and production ML decisions."
 description: Master ML system design interviews with Valerii Babushkin, ex-Meta Head
   of Data Science. Learn fraud detection systems, feature engineering, metrics selection,
   and production ML best practices for FAANG interviews.

_podcast/s07e08-from-data-science-to-data-engineering.md

@@ -4,13 +4,13 @@ guests:
 - ellenkonig
 description: Transition from data science to data engineering. Learn DevOps, CI/CD,
   collaboration skills, and cloud platforms. Career advice from Ellen König.
-intro: In this episode, Ellen König—Head of Engineering at alcemy—shares her journey
+intro: "In this episode, Ellen König—Head of Engineering at alcemy—shares her journey
   from software and data science to data engineering leadership. She explains why
   many professionals make the switch, the skills that matter most (from DevOps and
   CI/CD to collaboration), and how to prepare through side projects and software fundamentals.
-  Ellen also breaks down key tools like Git, Docker, and Airflow, discusses the realities
-  of cloud costs and team structures, and offers practical advice for anyone planning
-  a transition into data engineering.
+  <br><br> Ellen also breaks down key tools like Git, Docker, and Airflow, discusses
+  the realities of cloud costs and team structures, and offers practical advice for
+  anyone planning a transition into data engineering."
 ids:
   anchor: From-Data-Science-to-Data-Engineering---Ellen-Knig-e1fgfbm
   youtube: 3TTu-hYzxeg

_podcast/s08e02-hacking-your-data-career.md

@@ -2,14 +2,7 @@
 episode: 2
 guests:
 - marijnmarkus
-intro: In this episode, Marijn Markus—AI Lead and Managing Data Scientist at Capgemini—shares
-  how to stand out in data science by combining curiosity, courage, and creativity.
-  From his unconventional background in sociology and criminology, Marijn explains
-  how diverse teams outperform homogeneous ones, why proactive problem-solving matters,
-  and how to challenge hierarchy with data-driven insights. You’ll learn how to build
-  unique portfolio projects (like time series modeling from a coffee machine), apply
-  OSINT concepts to modern analytics, and grow your visibility through a thoughtful
-  LinkedIn strategy.
+intro: "In this episode, Marijn Markus—AI Lead and Managing Data Scientist at Capgemini—shares how to stand out in data science by combining curiosity, courage, and creativity. From his unconventional background in sociology and criminology, Marijn explains how diverse teams outperform homogeneous ones, why proactive problem-solving matters, and how to challenge hierarchy with data-driven insights. <br><br> You'll learn how to build unique portfolio projects (like time series modeling from a coffee machine), apply OSINT concepts to modern analytics, and grow your visibility through a thoughtful LinkedIn strategy."
 topics:
 - data science
 - career growth

_podcast/s08e06-recruiting-data-engineers.md

@@ -2,23 +2,23 @@
 episode: 6
 guests:
 - nicolasrassam
-intro: How do you hire data engineers in Europe today — and what should candidates
-  and hiring managers actually focus on during interviews? In this episode Nicolas
+intro: "How do you hire data engineers in Europe today — and what should candidates
+  and hiring managers actually focus on during interviews? In this episode, Nicolas
   Rassam, a Senior Talent Acquisition Partner at Helsing with 10+ years scaling AI
   and engineering teams at Onfido and Criteo, walks through the practical realities
-  of hiring data engineers across Europe’s competitive, borderless market. <br><br>
+  of hiring data engineers across Europe's competitive, borderless market. <br><br>
   We cover why data engineering matters now, differences in European hiring footprints,
   and the rising demand for modern tooling. Nicolas breaks down common hiring challenges
   — title ambiguity, experience mismatches, and recruiter technical literacy — and
-  explains how to evaluate transferable experience from software and BI roles. You’ll
+  explains how to evaluate transferable experience from software and BI roles. You'll
   get concrete guidance on level expectations (junior → senior), typical interview
   processes and assessments, resume essentials (SQL, Python, problem solving, outcomes),
   cloud fundamentals, when infrastructure/DevOps skills matter, portfolio/GitHub storytelling,
   and strategies for career switchers (internships, targeted projects). The episode
   also addresses hiring without degrees, industry fit for regulated data, and how
   targeted applications beat spray-and-pray. Listen to learn what to prepare for interviews,
   how to position projects, and what hiring teams really look for when recruiting
-  data engineering talent in Europe.
+  data engineering talent in Europe."
 ids:
   anchor: Recruiting-Data-Engineers---Nicolas-Rassam-e1hnkl1
   youtube: hylxiu4VGTo

_podcast/s09e04-freelancing-and-consulting-with-data-engineering.md

@@ -13,8 +13,7 @@ links:
   youtube: https://www.youtube.com/watch?v=9DTTrN-khCk
 season: 9
 short: Freelancing and Consulting with Data Engineering
-title: 'Freelance Data Engineering Playbook: Pricing, Client Acquisition & Tools —
-  Adrian Brudaru'
+title: 'Freelance Data Engineering Playbook: Pricing, Client Acquisition & Tools'
 transcript:
 - header: 'Guest Overview: Adrian’s Move to Freelancing'
 - line: This week we'll talk about freelancing in data engineering. We have a special
@@ -1274,22 +1273,22 @@ transcript:
   sec: 3776
   time: '1:02:56'
   who: Adrian
-intro: 'How do you price freelance data engineering work, win steady clients, and
-  pick the right tools for messy production problems? In this episode Adrian Brudaru
+intro: "How do you price freelance data engineering work, win steady clients, and
+  pick the right tools for messy production problems? In this episode, Adrian Brudaru
   — an economist-turned-business analyst who moved to Berlin, left corporate/startup
-  cycles to freelance for five years, and now co‑founds a data company releasing open
+  cycles to freelance for five years, and now co-founds a data company releasing open
   source tooling — walks through a practical playbook for freelance data engineers.
   <br><br> We cover pricing models (hourly rates, negotiation, occupancy and income
-  variability), client acquisition (networking, repeat business, recruiters vs direct
+  variability), client acquisition (networking, repeat business, recruiters vs. direct
   contracts, Upwork pros and cons), and scoping techniques (spikes, scope documents,
   managing expectations). Adrian also digs into technical topics: legacy cleanup,
   Airflow work, and a data loading tool for volatile schemas and automatic unpacking.
-  Along the way he explains building a reusable portfolio, transitioning from freelancing
-  to product or investing, working remotely vs on‑site, and how to create opportunities
+  Along the way, he explains building a reusable portfolio, transitioning from freelancing
+  to product or investing, working remotely vs. on-site, and how to create opportunities
   in local markets like Berlin. <br><br> Listen to learn concrete approaches to freelance
   data engineering pricing, client acquisition strategies, scoping projects, and practical
   tools to handle unstable schemas — so you can evaluate projects, set rates, and
-  grow a sustainable freelance practice.'
+  grow a sustainable freelance practice."
 ---
 Links: