Taro Book Club: Designing Data Intensive Applications - Chapter 4 (Encoding And Evolution)

Session Led by: [Lax Meiyappan](https://www.linkedin.com/in/lakshmanan-meiyappan/)

Topic: Chapter 4 - Encoding And Evolution

In this #taro-bookclub discussion, we'll be talking about Chapter 4 of [**"Designing Data-Intensive Applications"**](https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321) by Martin Kleppmann.

Here's the agenda:

- Let’s get to know each other with some quick introductions.
- We’ll break down and discuss the key concepts from Chapter 4.
- There will also be time for any questions and answers you might have.

Please send your questions ahead of time to [**Lax Meiyappan**](https://www.linkedin.com/in/lakshmanan-meiyappan/) in the [**Taro Premium**](https://www.jointaro.com/membership/) Slack, so that they can be included in the discussion.

Get ready for another engaging and informative session!

For SWE To ML Transitioners

In this final section on preparation strategy, we focus on how to transition successfully from software engineering into machine learning. Drawing on real experiences, we highlight practical, effective ways to stand out.

- **Lead with your strengths**: We already bring strong system design, coding, and production skills—qualities ML teams highly value. Hiring managers often prefer engineers moving into ML over scientists moving into engineering.
- **Leverage your current role**: Instead of relying only on side projects, we can grow by contributing to ML efforts at work—like model deployment, pipelines, or feature engineering.
- **Be selective with side projects**: One well-scoped project with clear business impact is far more compelling than several small tutorials.
- **Demonstrate your learning journey**: Show, don’t just tell—mention coursework, hands-on coding, and real applications of ML in your work.
- **Be honest and self-aware**: Interviewers appreciate curiosity and a growth mindset more than perfection. Let’s share where we are, what we’re learning, and how we’re applying it.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Engineering Managers Day-To-Day

In this lesson, we break down what an engineering manager’s day-to-day really looks like—why their calendars are packed with meetings, how they handle technical planning, and why the emotional side of the role is often underestimated.

- We highlight that EM calendars are dominated by 1:1s, alignment discussions, and cross-team negotiations, much of which shields us from logistical or resourcing debates.
- We explain that managers need enough technical depth to estimate project scope, evaluate dependencies, and participate meaningfully in planning without writing code themselves.
- We note that much of the real work happens outside meetings—reviewing docs, giving feedback, writing updates, and communicating progress upward to directors and VPs.
- We emphasize that people management is a huge portion of the job: coordinating collaboration, following up for junior engineers, resolving interpersonal issues, and handling performance challenges.
- We discuss the emotional burden managers carry handling compensation, performance ratings, personal struggles, and burnout and how this makes them part-time therapists as well as leaders.

Round Structure ML System Design

In this segment, we’re introduced to one of the most critical and career-defining parts of the ML interview process: **system design**. Especially at senior and staff levels, this round reveals our ability to lead and think holistically.

- **System design interviews evaluate both breadth and depth**—we’re expected to understand the full ML pipeline from data ingestion to deployment and monitoring.
- This round is less about solving isolated problems and more about **architecting end-to-end solutions** that operate under real-world constraints.
- We must demonstrate our ability to **make trade-offs** involving scalability, latency, noisy data, evolving product requirements, and long-term maintenance.
- Strong performance here often **distinguishes mid-level candidates from senior or leadership-level hires**.
- Ultimately, it’s our chance to show that we can **design ML systems that work, scale, and deliver value in production—not just in theory**.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Explanation Examples

In this extended segment, we see the CLEAR framework applied to real machine learning interview questions. Through examples, we’re shown how to craft strong, structured answers that go far beyond buzzwords or surface knowledge. This approach helps us communicate our understanding with clarity and depth.

- For a question like **"How do decision trees work?"**, we learn to walk through each part of CLEAR: define the concept, link to related ideas (e.g., random forests), provide real-world use cases, weigh pros and cons (e.g., interpretability vs. overfitting), and explain evaluation metrics (e.g., accuracy, RMSE).
- When comparing **CNNs vs. RNNs**, we avoid vague statements and instead explain each model’s core function, typical applications (e.g., image classification vs. time-series forecasting), and evaluation metrics, even including hybrid use cases for extra depth.
- For the common question **"How do you handle class imbalance?"**, we’re shown how to define the issue, link it to broader modeling challenges, provide realistic examples (e.g., fraud detection), explore trade-offs in techniques like resampling and reweighting, and select meaningful metrics like precision, recall, and AUC-ROC.
- We’re reminded that **bad answers often list jargon** without context or reasoning, while **great answers show structured thought**, connect theory to practice, and demonstrate the ability to make sound trade-offs.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

How Managers Hire Great Engineers [Part 17] - Push & Pull Factors

It's hard convincing great engineers to leave their current jobs. From this lesson, you will:

- Understand the importance of increasing your candidate acceptance rate, which can significantly reduce workload throughout the recruitment funnel
- Learn how to identify and leverage push and pull factors that influence a candidate's decision to leave their current role or join your company
- Discover the value of tailoring your closing approach to individual candidate motivations to enhance offer acceptance rates
- Gain insights into conducting honest Q&A sessions during the closing call to ensure a good fit, thereby increasing long-term retention on your team

Connect with John Egan on LinkedIn: <https://www.linkedin.com/in/jwegan/>

Amazon Behavioral Interview [Part 11] - Tell Me About A Time You Exceeded Expectations (Answer)

This video segment describes the interviewee's experience successfully increasing the acquisition of a business credit card at Costco.

- **Identifying an Opportunity:** The interviewee recognized the untapped potential of the business card, which offered higher profitability compared to the consumer card. They also identified a key differentiator: a higher gas rebate for the business card.
- **Leveraging Internal and External Factors:** The interviewee capitalized on the increased warehouse incentive for selling business cards and secured additional funding for training and in-store marketing.
- **Overcoming Challenges:**
  - **Internal Competition:** The interviewee faced resistance from the consumer card division, who perceived the increased business card acquisition as cannibalizing their own customer base.
  - **External Constraints:** The interviewee navigated challenges related to Costco's limited marketing space and restrictions on in-store promotions.
- **Significant Results:** Despite these challenges, the interviewee successfully tripled the initial acquisition target, demonstrating strong project management and execution skills.

This example highlights the interviewee's ability to identify and capitalize on opportunities, overcome internal and external challenges, and achieve significant results.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Ship & Grow 500k+ User Side Projects [Part 20] - When To Give Up

Side projects can give you massive, career-changing, *life-changing* benefits. However, in order for this to happen, you need to put in a lot of work. This lesson teaches you:

- The balance you need to strike when it comes to working on side projects, particularly which ones you work on
- The minimum amount of time you need to give a side project that you genuinely believe in
- A detailed list of criteria you can use to figure out whether you should abandon a side project
- Why failed side projects are actually good for you

Amazon Behavioral Interview [Part 17] - Handling Unclear Expectations From Leadership (Answer)

This video segment describes a situation where the interviewee faced conflicting demands from leadership: the need to drive customer satisfaction and improve resolution times while also maintaining employee engagement and retention.

Here are the key takeaways:

- **Identified the Conflict:** The interviewee recognized the conflicting demands from leadership regarding customer satisfaction metrics and employee well-being, particularly during a challenging period with COVID-19.
- **Navigated the Challenges:**
  - **Open Communication:** They fostered open communication with their team, actively seeking feedback and addressing concerns.
  - **Prioritized Employee Well-being:** Despite the pressure to meet performance metrics, they prioritized employee well-being by:
    - Implementing measures to reduce stress, such as virtual happy hours and flexible work arrangements.
    - Actively listening to employee feedback and addressing their concerns.
  - **Balanced Priorities:** They successfully navigated the competing demands by finding a balance between meeting performance targets and maintaining employee morale.
- **Demonstrated Leadership:** The interviewee demonstrated strong leadership by:
  - Proactively addressing the challenges and seeking solutions.
  - Effectively communicating expectations and priorities to their team.
  - Building trust and fostering a supportive team environment.

This example highlights the interviewee's ability to navigate complex situations, prioritize competing demands, and effectively lead and motivate their team.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Why Engineering Manager Interviews Are Hard

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

Many engineers find EM interviews difficult because they lack direct experience with the interview process, often having been promoted internally or being out of practice. The expectations for EM roles center around leadership, strategy, and stakeholder management rather than technical execution.

- **Limited Interview Experience:** Most engineering managers either transition through internal promotions or haven't interviewed for years, leading to a lack of familiarity with the process.
- **Different Decision-Makers and Focus:** Unlike IC roles, EM interview decisions are made by senior leadership who prioritize business strategy, people management, and stakeholder alignment over technical details.
- **Behavioral Focus:** Behavioral rounds dominate the EM interview process (about 80%) because they assess how candidates handle adversity and complex interpersonal dynamics.
- **No “LeetCode” for EM Interviews:** The subjective nature of these interviews makes it difficult to prepare using standard methods—there’s no one-size-fits-all approach to practicing answers.
- **Feedback is Rare and Vague:** Candidates often don't receive clear rejection reasons, leaving them uncertain about what to improve or how well their responses aligned with expectations.

Well Roundedness

Working in Big Tech expands far beyond engineering; it shapes individuals into well-rounded professionals by pushing them to develop skills in communication, leadership, and cross-functional collaboration.

- **Development of cross-functional skills**: Navigating product launches or technical changes in a large organization requires coordination across many stakeholders, honing project management, communication, relationship-building, and system design abilities.
- **Environment fosters personal growth**: Operating within a large, complex structure forces individuals to adapt, influence, and lead through collaboration—skills less emphasized in smaller, more agile companies.
- **Preparation for an AI-driven future**: As coding becomes more automated, the remaining 80% of a top engineer’s job like communication, strategy, and leadership becomes even more valuable. This environment equips engineers for longevity in their careers.
- **Transferable experience for career switchers**: Even those new to tech can succeed by leveraging skills from prior careers (e.g., hospitality, education, operations). Communication and stakeholder management experience, in particular, translate well.
- **Resilience through versatility**: Focusing on foundational skills that transcend tech stacks or tools creates career durability, avoiding dependency on niche or outdated technologies.

The AI Frontier: Get Paid To Build Smarter LLMs

The future of AI is being built by a handful of companies: OpenAI, Anthropic, and DeepMind, among others. One of the core ingredients for improving the performance of LLMs is the use of reinforcement learning from human feedback (RLHF).

Join us for a live conversation with Mercor's cofounder and CTO, [**Adarsh Hiremath**](https://www.linkedin.com/in/adarsh-h-623941204) hosted by Taro's cofounder and CEO, [**Rahul Pandey**](https://www.linkedin.com/in/rpandey1234/). Mercor is a key partner to enable RLHF for many frontier model companies. As part of this, Mercor offers 1000s of part-time, remote-friendly roles with competitive compensation.

This is a new type of white-collar work that involves debugging, validating, and creating scenarios for LLMs. As the capabilities of models improve, the expertise required to train models keeps increasing. One of the highest-priority areas is around coding, which means Software Engineers have a special role to play in this type of work.

This event explains the pros and cons of AI lab gig work, what exactly the work entails, and how much you can earn.

Some topics we’ll cover:

- Typical hourly rates and the compensation model
- Project-to-project variability
- Unique challenges of remote collaboration in this type of work
- What does the day-to-day look like with this type of work?
- How does this work impact the development of LLMs and AI agents?
- How will this work evolve over the next 5 years?

There will also be time for open Q&A from the audience.

Mercor is an AI-powered recruiting platform that has become one of the largest sources for human labor for foundation model companies. Mercor has raised more than $100 million dollars and is located in San Francisco, CA. Explore [**relevant jobs on Mercor here**](https://work.mercor.com/?referralCode=91aab11f-ce55-40b3-9c2d-f74576cac28f) (100+ Software Engineering jobs available).

In this segment, we explore the importance of **Exploratory Data Analysis (EDA)** in machine learning interviews, especially during notebook-based exercises. This is a key moment to distinguish ourselves by demonstrating analytical depth and real-world intuition.

- We move beyond just **checking for missing values or printing basic stats**, which signals a surface-level approach.
- Instead, we analyze **feature distributions**, identifying skewness, outliers, and unusual patterns that may affect modeling.
- We examine **relationships and interactions between variables**, not just computing correlations, but exploring potential dependencies and dynamics.
- We assess **data quality**, including duplicates, inconsistent categorical values, and signs of data leakage.
- This deeper EDA shows that we can **interrogate the data critically**, a trait that interviewers associate with strong, production-ready ML practitioners.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

How Managers Hire Great Engineers [Part 11] - Meetups and Conferences

When it comes to relationship building, you simply cannot beat in-person interaction. From this lesson, you will:

- Understand the value of making personal connections at meetups and conferences to stand out from the noise of digital outreach
- Learn strategies for building a network by engaging with people at events, collecting their contact information for future outreach
- Discover how creating and hosting your own events can establish your brand as a company and attract potential candidates
- Realize the benefits of long-term recruitment through sustained relationships with event attendees and speakers
- Explore how relationships with speakers at your events can evolve into hiring opportunities for top talent in the future

Connect with John Egan on LinkedIn: <https://www.linkedin.com/in/jwegan/>

Become An Engineer Influencer [Part 12] - Engineering Topics

He Bought Coffee For ALL Meta Employees On His Last Day

How Managers Hire Great Engineers [Part 12] - Outreach Basics

So you've found a great candidate - Now it's time to get them into your interview loop. From this lesson, you will:

- Understand the benchmark for a good response rate when sourcing candidates, with 15% as the target for moving candidates forward in the recruitment process
- Learn that email outreaches typically yield higher response rates than LinkedIn InMails and why finding a candidate’s personal email can be more effective
- Discover the advantages of outreach coming directly from an engineering manager rather than a recruiter to significantly increase the likelihood of a response
- Explore tools and methods for finding candidate emails, including using services like Clearbit and experimenting with Gmail to predict email addresses
- Gain insights into how personalizing the outreach process by handling initial replies and interactions can make a meaningful impact on recruitment outcomes

Connect with John Egan on LinkedIn: [https://www.linkedin.com/in/jwegan/​](https://www.linkedin.com/in/jwegan/%E2%80%8B)

Taro Book Club: Designing Data Intensive Applications - Chapter 3

Topic: Chapter 3 - Storage and Retrieval

In this #taro-bookclub discussion, we talk about Chapter 3 of [**"Designing Data-Intensive Applications"**](https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321) by Martin Kleppmann.

Here's the agenda:

- Let’s get to know each other with some quick introductions.
- We’ll break down and discuss the key concepts from Chapter 3.
- There will also be time for any questions and answers you might have.

Thanks to [**Suraj Singh**](https://www.linkedin.com/in/suraj-singh-495b192b/) and [**Sangavi Pari**](https://www.linkedin.com/in/sangavi-pari/) for leading this session!

Hi, I’m Alex

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

In this video, I introduce myself and share my mission to help tech leaders thrive, which is a passion of mine.

- I have over 18 years of experience in the tech industry, including more than 8 years in leadership roles, and I graduated with a bachelor's in software engineering from the University of Waterloo.
- My goal is to assist individuals in becoming better versions of themselves through effective leadership. I encourage viewers to engage with me on this journey of growth and development.

The Case AGAINST Big Tech

We discuss reasons why Big Tech may not be the right fit for everyone, especially as career goals and personal priorities evolve over time.

- **High Work-Life Balance Needs**: For those with growing personal commitments—like family, health, or other priorities—Big Tech’s demanding environment may no longer be sustainable or fulfilling.
- **Low Tolerance for Pressure**: The performance-driven, competitive nature of Big Tech can be overwhelming for those who prefer a calmer, less intense work atmosphere.
- **Preference for Independent, Heads-Down Work**: Engineers who thrive in solo coding environments without heavy documentation, collaboration, or politics may find the collaborative and process-heavy style of Big Tech frustrating.
- **Avoidance of Office Politics**: Success often requires political navigation, influence-building, and strategic communication—elements that don't appeal to everyone.
- **Desire for Simplicity**: Not everyone aspires to climb the corporate ladder; for those who just want to write good code without excessive overhead, there are better environments than Big Tech.

Tech Career Growth Videos

Our instructional videos provide software engineers with insights needed for career growth. Masterclasses go incredibly in-depth on a specific tech topic while case studies are real examples from established engineering leaders.