Ashok Srinivas, SVP of Engineering at Aledade, joins The Tech Trek to break down what it really means to have impact as an engineering leader. With experience at Microsoft, Snapchat, Indeed, Dropbox, and now Aledade, Ashok brings clarity on how to assess your value, earn trust, and align technical strategy to business outcomes. Whether you're leading at a scrappy startup or an enterprise giant, this conversation offers a grounded and practical lens on leading with purpose, adjusting your playbook, and knowing when to pivot.

Key Takeaways

• Your first 90 days as a leader should be about listening, learning the culture, and earning trust

• Technical strategy only matters if it maps to business value—long-term bets need short-term execution

• Engineering leadership changes based on company stage: wartime vs peacetime, scale vs speed

• Culture and resilience matter more than expertise—especially in remote, high-change environments

• Great leaders don't just bring the right tools—they know when to use them, and when to stay curious

Timestamped Highlights

00:36 — What Aledade does and why healthcare impact is personal

02:14 — From chip design to engineering leadership: Ashok’s career journey

04:09 — Matching your leadership style to company stage and market dynamics

06:39 — Why trust-building matters more than early change-making

10:24 — How Ashok evaluates engineering impact across people, product, and execution

13:13 — The thrill of learning new business models—and why he keeps switching industries

16:41 — Aligning OKRs with team performance while still shipping hands-on

21:51 — The most underrated skill in engineering orgs: resilience in the face of ambiguity

Quote of the Episode

“Strategies change all the time. If your team isn’t aligned through culture, they won’t be ready to pivot—and that’s what really holds you back.” — Ashok Srinivas

Resources Mentioned

• Radical Candor by Kim Scott

Call to Action

Enjoyed the episode? Share it with an engineering leader you respect. Then subscribe to The Tech Trek so you never miss conversations like this—real insights from people building the future.

Vijaye Raji, CEO and founder of Statsig, left two decades of success at Microsoft and Facebook to start from scratch—at age 41. In this episode of The Tech Trek, we unpack the mindset, planning, and trade-offs that come with becoming a first-time founder later in life. If you've ever wondered what it really takes to leave the safety of big tech to chase a startup dream, this one’s for you.

What You’ll Learn

• Why Vijaye treated the decision to become a founder separately from the idea for Statsig

• How he de-risked the leap by financially preparing his family for the journey

• The emotional rollercoaster of being a solo founder—and how he stays grounded

• The biggest blind spots coming from big tech to startup life (hello, sales and SOX compliance)

• How he thinks about pivoting, product strategy, and avoiding the “limping-along” trap

Timestamps to Catch

02:03 – Why he walked away from Meta and Microsoft

04:32 – The real difference between “wanting to start a company” and knowing what to build

06:17 – How he set a 10-year plan—and avoided the dangerous middle zone

11:54 – What he didn’t know until he had to do it himself: sales, marketing, compliance

15:28 – How he structured support at home to take the leap without a co-founder

21:40 – Tactical advice for future founders to build toward entrepreneurship intentionally

Quote of the Episode

“Startup is not an individual affair—it’s a family affair. It affects people around you in subtle ways, and some not so subtle.”

Resources Mentioned

• Statsig: https://www.statsig.com

• Connect with Vijaye on LinkedIn: https://www.linkedin.com/in/vijaye

Pro Tip from Vijaye

If you’re planning to start a company in the next five years, structure your career today to pick up the missing skills: sales, marketing, financials, hiring, and firing. Be intentional about it.

Enjoyed the episode?

Follow The Tech Trek for more real conversations with startup builders, tech leaders, and product thinkers. Like, subscribe, and share this episode with someone who’s thinking about taking the leap. And if you’ve got thoughts or feedback—drop a comment or connect on LinkedIn.

What if your data science team could drive business outcomes across products, not just models? In this episode, Hicham El-Hassani shares a tested blueprint for building data teams that are adaptable, retention-proof, and ready to ship.

With 18 years of experience, Hicham has led high-impact data science orgs across insurance and software—and he’s not afraid to challenge the standard playbook. He explains why most teams fail to scale, how generalist data scientists can outperform specialists, and what actually matters in model success (hint: it’s not just the algorithm).

Whether you’re a technical leader, hiring manager, or data practitioner, this conversation is packed with insights on how to design for execution, avoid attrition, and get your models into production—fast.

Key Takeaways

Data science orgs need flexible, crew-style structures—not rigid vertical silos

Generalists thrive when given exposure, ownership, and tailored training

Feature engineering and domain context often beat algorithm tuning

Execution and documentation matter more than flashy tools

GenAI will boost productivity—but won’t replace real data science judgment

Timestamped Highlights

02:00 — Why rigid, specialized teams backfire in data orgs

06:45 — The real value of domain knowledge and how to build it quickly

11:50 — How data scientists can shape sales, pricing, and go-to-market strategy

17:30 — A four-phase matrix to structure projects and reduce context switching

23:00 — How AI tools are already speeding up DS workflows (and what’s next)

26:00 — One habit that separates scalable teams from forgettable ones

Quote of the Episode

"Cross-pollination is the best thing—when data scientists are exposed to different business problems, they evolve faster and stay longer."

Call to Action

Enjoyed the conversation? Share this episode with someone building or managing a data team. And if you haven’t yet, subscribe to The Tech Trek for more no-fluff insights from leaders building the future of tech.

What does the “long tail” of AI really look like in a highly regulated industry? In this episode, Dave Wollenberg, VP of Enterprise Data & Analytics at Scan, breaks it down. From cautious experimentation to enabling non-technical users to build AI-driven POCs, Dave shares a grounded, practical perspective on AI adoption inside a Medicare Advantage organization.

You’ll hear why the real transformation isn’t just technical—it’s cultural. We talk about how to shift employee mindsets, educate business teams, and unlock self-service analytics while staying compliant. If you’re a tech or data leader trying to separate hype from real value, this one’s for you.

Key Takeaways:

The long tail of AI means rethinking roles—not just automating tasks

Real AI enablement starts with data quality, governance, and semantic clarity

Non-technical employees can (and should) build AI proof-of-concepts

Change management will make or break your AI strategy

In regulated industries, open source and secure deployment models matter

Timestamped Highlights:

00:55 – What Scan Health Plan does and why AI matters in healthcare

03:10 – From machine learning to generative AI: how use cases have evolved

08:15 – Three types of business users and how to upskill them for AI

12:40 – Shifting expectations: stakeholders want AI-powered insights, now

15:20 – Why self-service BI still falls short without a solid data foundation

18:35 – AI adoption isn’t just IT’s job—business users need to lead too

22:15 – Navigating AI in regulated industries: risks, rules, and realities

Quote of the Episode:

“It's not as if there's a certain amount of work in the world, and if AI takes some, there's nothing left to do. When you make people more powerful, they add more value—and you want more of them, not fewer.”

Pro Tips:

Host internal hackathons to build excitement and break down resistance

Use sandbox environments to safely encourage experimentation

Don't wait for technical users—give your business teams the tools to try

Call to Action:

Like what you heard? Share this episode with someone exploring AI adoption in their org. Subscribe to The Tech Trek for more candid conversations with tech leaders on building, scaling, and leading through change.

What happens when you bring Silicon Valley tech thinking into an “unsexy” industry? Alex Jekowsky, Co-founder and CEO of Cents, shares how his vertically integrated platform is quietly transforming garment care—starting with laundromats. In this conversation, Alex breaks down what it takes to digitize an analog industry, earn operator trust, and build deep value with a lean team. If you’ve ever wondered what it really means to build vertical SaaS for SMBs, this is a masterclass.

Key Takeaways

Start with digitization, not disruption—operators don’t need revolution, they need visibility and options.

Building for SMBs means listening first, innovating later. Reliability beats cleverness early on.

A lean team can deliver better quality by being more deliberate, but it comes with execution risk.

Cents’ growth isn’t about horizontal expansion—it’s about going deeper with each customer.

Clear alignment on mission—“garment care”—enables scale without complexity.

Timestamped Highlights

[01:50] – Why laundromats? The overlooked opportunity in an “unsexy” industry

[06:30] – Digitize first, then provide optionality: Cents' real value proposition

[09:40] – Why innovation is an earned right in SMB SaaS

[12:50] – The tradeoffs and benefits of building vertically with a small team

[16:40] – How Cents plans to grow deeper in garment care without chasing new verticals

[21:50] – Culture, clarity, and staying anchored to the mission—how Cents keeps its edge

Quote of the Episode

"Nobody works with you because you're innovative—they work with you because you work."

Resources Mentioned

Cents: https://www.trycents.com

Call to Action

If this episode changed how you think about vertical SaaS or SMB tech, share it with a founder or product leader who needs to hear it. And don’t forget to follow The Tech Trek for more behind-the-scenes stories on building products that actually move industries forward.

How do you turn GenAI excitement into real enterprise value—without leaving people behind?

In this episode, Amir talks with Mike Urban, Chief Technology Operations Officer at Best Egg, about the overlooked muscle every company needs to build: change management. Mike shares how his team is navigating the real-world complexity of bringing GenAI into production across a highly regulated fintech org—while aligning control and risk teams as unexpected champions of innovation.

If you’re trying to move fast without breaking trust, this conversation is packed with lessons.

Key Takeaways:

Change management isn’t a framework—it’s a living process, just like the changes you’re navigating.

GenAI adoption starts with personalized enablement, not just tooling. Everyone has a different “light switch.”

Risk and control functions can be powerful allies in innovation, not blockers—if brought in early.

Gamified onboarding and grassroots advocacy can shift perception and accelerate adoption.

The real value of GenAI isn’t replacement—it’s amplification. Think "thought partner," not "automation engine."

Timestamped Highlights:

00:47 – What Best Egg does and who they serve in the fintech landscape

01:46 – Why traditional change management often fails in tech orgs

07:42 – The GenAI learning curve: why every employee needs their own light switch moment

10:18 – Risk and control teams as enablers of innovation (not roadblocks)

12:39 – A clever GenAI onboarding experiment with Best Egg’s control team

17:01 – Framing GenAI as a productivity co-pilot, not a job replacer

22:33 – Why GenAI's constant evolution might actually make it easier to adopt

Quote of the Episode:

“Every person has their own GenAI light switch—and once it’s on, it doesn’t turn off.”

Call to Action:

If this episode sparked new ideas for how your team can embrace GenAI more effectively, share it with a colleague or drop us a review. And don’t forget to subscribe so you never miss an episode of The Tech Trek. You can also connect with Mike Urban on LinkedIn to continue the conversation.

What does it take to build and scale AI responsibly inside a major media organization?

In this episode, Amir sits down with Arvind Thinagarajan, formerly Head of Enterprise Data Science & Analytics at Gannett (publisher of USA Today), to unpack how they built a volunteer-driven AI Council that governs, guides, and accelerates AI initiatives across the company.

From prioritization frameworks to cross-functional subcommittees, Arvind shares the inner workings of a model that supports nearly 90 AI use cases — and might just inspire how your org tackles AI at scale.

Key Takeaways

– Gannett’s AI Council is fully volunteer-based and cross-functional, giving every department a voice in how AI gets used.

– New AI ideas bubble up from across the org — the council exists to prioritize and support them, not to own or build them.

– Every pilot starts with a scoped business case, clear success metrics, and a timeline.

– The enterprise rollout phase is intentional — ensuring tech used in pilots aligns with the broader IT stack.

– A separate IT Council works alongside the AI Council to avoid duplicate tools and ensure strategic alignment.

Timestamped Highlights

00:57 – What the AI Council is and how it’s structured

03:54 – How the council started and why it matters in media

06:19 – Subcommittees that cover everything from tooling to compliance

09:40 – Where AI use cases come from (hint: it’s not top-down)

11:39 – Who actually builds the solutions, and how governance plays out

16:55 – 87+ tracked use cases and what happens after a pilot succeeds

Quote of the Episode

“The AI Council exists to make sure there are no silos. We’re here to bring the right skill sets, tool sets, and mindsets together — to solve the right problems, the right way.”

Call to Action

If you’re working on AI in a complex org — or trying to sell into one — this episode will give you the playbook from inside a company doing it right.

Follow the show on your favorite podcast platform, share it with a teammate, and if you liked the episode, leave a quick review. It really helps.

What does it take to build AI systems that are private by design—and ready for tomorrow’s regulations?

In this episode, I’m joined by Rishabh Poddar, CTO and Co-founder of Opaque Systems, to explore how data privacy, compliance, and AI innovation intersect in a rapidly evolving landscape. Rishabh breaks down the impact of emerging privacy laws, the risks with agentic AI systems, and why building cryptographic guarantees into the foundation of your AI stack isn’t optional—it’s essential.

Whether you're deploying AI at scale or just experimenting, this conversation will challenge how you think about trust, governance, and the future of responsible AI.

🔑 Key Takeaways

Data privacy laws are tightening—and conflicting with the growing demand for more diverse data to train AI models.

Agentic AI introduces new risk: non-deterministic systems that act independently demand a fresh approach to guardrails and governance.

Opaque Systems' platform keeps sensitive data encrypted throughout the full AI lifecycle, with cryptographic auditability and verifiable access controls.

The future is about adaptability—not one-size-fits-all compliance, but giving organizations the tools to meet evolving laws.

Enterprise example: ServiceNow reinvented their internal helpdesk with agentic AI while preserving strict internal data boundaries.

⏱ Timestamped Highlights

[00:34] – What is Opaque Systems? The confidential AI platform built from UC Berkeley research.

[01:31] – How privacy laws and AI demands are on a collision course—and why that tension led to Opaque’s creation.

[06:24] – The shift from structured data governance to unstructured, agent-driven challenges.

[10:43] – Why humans breaking trust feels different than AI—and how to build for trust without false assumptions.

[15:30] – Real-world case study: How ServiceNow uses AI + Opaque to safeguard confidential data at scale

[21:18] – You can’t wait for laws to settle—future-proof AI systems must offer customizable compliance tools today.

💬 Quote of the Episode

“Privacy and security need to be baked into the design of a system from the ground up—so you’re protected no matter how the laws evolve.”

🛠 Pro Tips

If you’re designing AI applications for enterprise use, map out where data flows—then assume each step is a risk zone.

Invest early in systems that offer auditable, verifiable data usage—this becomes your insurance as laws change.

Don’t assume your platform team should (or can) see all the data. Siloed access isn't a bug—it’s a feature for privacy.

📢 Call to Action

Enjoyed this episode? Follow the show, leave a review, and share it with someone working on AI governance or data privacy. Got questions or thoughts? Reach out to Amir on LinkedIn—he’d love to hear from you.

New episodes drop weekly. Subscribe wherever you listen.

What happens when a data-driven founder leaves Big Tech to tackle a broken healthcare system?

In this episode of The Tech Trek, Amir sits down with Tim Edgar, Co-founder and CTO at Venteur, to unpack how deep personal insight, emotional connection, and data all play a role in identifying real-world problems worth solving. From launching Bing under Satya Nadella to co-founding companies with his sister, Tim shares how his experience across startups and Microsoft shaped his approach to product, purpose, and people.

This one is for builders, operators, and tech professionals who want to do more than just ship features—they want to build something meaningful.

🔑 Key Takeaways:

Startups are fueled by personal conviction: Tim’s best ideas come from emotional moments backed by hard data.

The best founders learn like anthropologists: Talk to people. Watch their reactions. Treat them like masters of their craft.

Complementary co-founders create creative tension: Tim and his sister bring radically different lenses—and that’s their strength.

Big Tech can be an entrepreneurial training ground: Microsoft gave Tim exposure to data scale and mentorship he still draws on today.

Vision + Next Step > Five-Year Plan: Tim shares why obsessing over the next move while keeping an eye on the North Star is his go-to strategy.

⏱️ Timestamped Highlights:

[02:03] – Tim shares the origin story of his first startup—and how a $5 power bill unlocked a much bigger mission.

[03:14] – How growing up with small business owners and private insurance shaped his view of healthcare.

[06:29] – What Tim listens for when validating a problem with customers (hint: it's all about emotion and nuance).

[09:10] – Inside early Microsoft: pitching ideas to Satya Nadella and learning entrepreneurship from within Big Tech.

[13:26] – Building with family: why working with his sister as co-founder has been a superpower, not a liability.

[22:19] – “Vision and next step”: How Tim defines success and stays focused without overplanning the future.

💬 Quote of the Episode:

“Startups are hard. If there’s not some emotional resonance and data that proves it’s worth solving, you won’t have the fuel to keep going.”

🛠️ Resources Mentioned:

Venteur (https://www.venteur.com)

✅ Pro Tips:

When validating an idea, don’t just ask surface-level questions. Let people teach you. Observe their reactions. That’s where the real signals are.

Don’t overlook Big Tech as a training ground. If used intentionally, it can be a launchpad for your founder journey.

🙌 Call to Action:

If you found this episode valuable, share it with a fellow tech leader who’s navigating the leap from ideas to impact.

And don’t forget to subscribe, leave a review.

In this episode of The Tech Trek, Amir sits down with Davy Li, Head of Engineering at Mesa, a startup redefining how homeowners earn rewards on everyday home expenses. Davy shares his personal journey from Big Tech to startup life, unpacks how he’s built Mesa’s engineering team from scratch, and offers a refreshingly candid look at what it means to be an effective leader in a small but growing organization.

From defining cultural values to hiring without a brand name, Davy drops wisdom on leadership, team modeling, and giving engineers the freedom to thrive. If you're building or leading teams in tech—or planning to—this one's packed with insights you can act on today.

🧠 Key Takeaways:

Culture is foundational: Davy built Mesa’s engineering culture before hiring anyone—trust, simplicity, transparency, and bias for action anchor the team.

Give great engineers space: The best devs aren’t waiting on tickets—they’re engaging directly with stakeholders and proposing their own solutions.

Small teams = shared ownership: Davy empowers his team by assigning champions for different technical domains and pushing people outside their lanes.

Startup hiring is scrappy: Without a big brand, Davy had to hustle through referrals and review hundreds of candidates to land A-players.

Upskilling never stops: Whether through reading, podcasts, or trial-by-fire, Davy believes learning is a muscle that needs constant exercise.

🕒 Timestamped Highlights:

(00:40) What Mesa does and how their homeowner rewards work

(02:09) Adjusting from Big Tech to startup: different challenges, different muscles

(03:40) The four cultural values that define Mesa’s engineering org

(06:57) How being closer to the business shifts the way you lead engineering

(09:09) What Davy prioritized when modeling the team from scratch

(11:14) Creating space for engineers to solve real business problems

(13:14) Borrowing from Cash App and Spotify: PR auto-approvals, DRIs, and ownership

(16:50) How Davy closes skill gaps as a manager—and why trying new things matters

(18:31) Promoting psychological safety and cross-functional growth on the team

(21:14) Why he's more hands-on with code now than ever before

(22:47) Startup hiring realities: no brand, no inbound, just grit

(24:55) Best way to reach Davy if you want to connect

💬 Quote to Remember:

“The fastest way to a good idea is to have a lot of ideas—and the fastest way to grow is to just try things.”

🧩 Leadership Tips (from the episode):

• Create psychological safety: Failure should be normalized. That’s how teams experiment and grow.
• Start with culture, not code: Even in the earliest hiring stages, define how you want your team to operate.
• Push responsibility downward: Assign DRIs and let engineers own the full scope of a project—not just the tickets.
• Hire for slope, not just skills: A-players attract A-players, but only if you're willing to dig deep and sell the mission.