The Tech Trek

Afrooz Ansaripour, Director of Data Science at Walmart, joins the show to explain how global leaders are shifting from simple historical tracking to predicting psychological triggers and customer intent. This episode explores the evolution of customer intelligence and how Generative AI is turning massive data sets into personalized, value driven experiences. Listeners will learn how to balance hyper personalization with foundational privacy to build lasting consumer trust.

Key Insights

Predict intent rather than just reporting past transactions to understand why a customer is with the brand.

Use Generative AI as an explainability layer to transform complex data platforms from black boxes into conversational tools.

Prioritize customer trust as a critical part of the user experience rather than just a legal requirement.

Integrate digital and physical signals to create a 360 degree view that reveals insights which would otherwise be invisible.

Focus on rapid technology adoption and curiosity as the primary drivers of success in modern AI teams.

Timestamped Highlights

01:51 Identifying the challenges and opportunities when managing millions of real time signals.

06:43 Strategies for showing genuine value to the customer without making them feel like just a part of a sale.

09:51 How LLMs are fundamentally changing the way data teams interpret unstructured feedback and behavioral patterns.

14:42 Managing privacy and ethical data practices while building personalized conversational AI.

19:14 Stitching together the online and offline journey to create a seamless customer experience.

22:52 The necessary evolution of data science skills toward storytelling and execution bias.

A Powerful Thought

"Personalization should never come at the expense of customer trust."

Tactical Steps

Combat the garbage in garbage out problem by refining cleaning processes to handle modern AI requirements.

Build an interactive layer or chatbot on top of data products to make insights instantly accessible and automated.

Translate technical insights into real world decisions to ensure customers actually benefit from data models.

Next Steps

Subscribe to the show for more insights into the future of tech. Share this episode with a peer who is currently navigating the complexities of customer data.

Shahryar Qadri, CTO of OneImaging, joins me to unpack a hard truth about healthcare tech: the goal is not to remove humans, it is to give them more room to be human.

We talk about where cost “optimization” actually helps patients, why radiology is a perfect fit for AI but still held back by data access, and how better workflows can improve trust, speed, and outcomes without losing the human touch.

OneImaging sits in the radiology benefits space, helping members book imaging in a national network with more transparency and a high touch booking experience, while helping employers cut imaging costs significantly.

Key takeaways

• The “human touch” in healthcare is not going away, the better play is using tech to increase capacity so caregivers can spend more time being caregivers

• Cost optimization is not always about paying less for expertise, it is often about wasting less human time, improving trust, and removing friction around services

• Healthcare still runs on outdated plumbing in places you would not expect, including fax based workflows that slow everything down

• Radiology is one of the best real world use cases for AI, but the bigger blocker is getting access to imaging data in usable form, not model capability

• Your health data is already “there”, but it is not working for you yet. The next wave is tools that scan your longitudinal record and surface what to ask your doctor about, so you can be a stronger advocate for your own care

Timestamped highlights

• 00:36 What OneImaging actually does, and why “transparent imaging” is more than a pricing story

• 02:00 Why healthcare stays personal, and how tech should increase capacity instead of replacing care

• 03:36 The real definition of cost optimization, commodity versus service, and where trust matters

• 07:01 The surprising reality of imaging ops, why it still feels like 1998, and what gets digitized next

• 17:19 AI in radiology is real, but the data access and interoperability gap is the bottleneck

• 24:21 Your CDs are full of value, the problem is we do almost nothing with that data today

A line worth replaying

“These LLM models are the worst that they’ll ever be today. They’re only going to get better and better and better.”

Call to action

If this episode sparked a new way of thinking about healthcare tech, follow The Tech Trek on your podcast app, share it with a friend in product or engineering, and connect with me on LinkedIn for more conversations like this.

Swarupa Mahambrey, Vice President of Software Engineering at The College Board, breaks down what tech debt really looks like in a mission critical environment, and how an engineering mindset can prevent it from quietly choking delivery. She shares a practical operating model for paying down debt without stopping the roadmap, and the cultural habits that make it stick.

You will hear how College Board carved out durable space for engineering excellence, how they use testing and automation to protect reliability at scale, and how to make the trade offs between features, simplicity, and user experience without slowing the team to a crawl.

Key Takeaways

• Tech debt behaves like financial debt, delay the payment and the interest compounds until even simple changes become painful

• A permanent allocation of capacity can work, dedicating 20 percent of every sprint to tech debt can reduce support load and improve delivery

• Shipping more features can slow you down, simplifying workflows and validating with real usage can increase velocity and reduce tickets

• Resilience is not about avoiding every failure, it is about designing for graceful degradation so spikes and outages become small blips instead of crises

• Automation is not “extra,” it is part of the definition of done, including unit tests as acceptance criteria and clear code coverage expectations

Timestamped Highlights

• 00:00 Why tech debt is a mindset problem, not just a backlog problem

• 01:00 Tech debt explained with a real example, what happens when a proof of concept becomes production

• 03:45 The feature trap, how “powerful” workflows can overwhelm users and explode maintenance costs

• 11:03 Engineering Tuesday, one day a week to strengthen foundations, not ship features

• 14:39 Stability vs resilience, designing systems that bend instead of shatter

• 20:06 Testing and automation at scale, unit tests as a requirement and code coverage guardrails

A line worth keeping

“If we don’t intentionally carve out space for engineering excellence, the urgent will always crowd out the important.”

Practical moves you can steal

• Protect a fixed slice of capacity for tech debt, make it part of the operating model, not a one time cleanup

• Treat automation as acceptance criteria, no test, no merge, no release

• Use pilots and targeted releases to learn early, then iterate based on metrics and real user behavior

• Design for graceful degradation with retries, fallback paths, and clear failure visibility

Call to action

If this episode helped you think differently about tech debt and engineering culture, follow The Tech Trek, leave a quick rating, and share it with one engineer who is fighting fires right now.

Software is still eating the world, and AI is speeding up the clock. In this episode, Amir talks with Tariq Shaukat, co CEO at Sonar, about what it really takes for non tech companies to build like software companies, without breaking trust, security, or quality.

Tariq shares how leaders can treat AI like a serious capability, not a shiny add on, and why clean code, governance, and smart pricing models are becoming board level topics.

Key Takeaways

• “Every company is a software company” does not mean selling SaaS, it means software is now core to differentiation, even in legacy industries.

• The hardest shift is not tools, it is mindset: moving from slow, capital style planning to fast iteration, test, learn, and ship.

• AI works best when leaders stay educated and involved, outsourcing the whole strategy is a real risk.

• “Trust but verify” needs to be a default posture, especially for code generation, security, and compliance.

• Pricing will keep moving toward value aligned consumption models, not simple per seat formulas.

Timestamped Highlights

• 00:56 What Sonar does, and why clean code is really about security, reliability, and maintainability

• 05:36 The Tesla lesson: mechanics commoditize, software becomes the experience people buy

• 09:11 Culture plus education: why software capability cannot live in one silo

• 14:21 Cutting through AI hype with program discipline and a “trust but verify” mindset

• 18:23 Boards, governance, and setting an “acceptable use” policy for AI before something goes wrong

• 25:18 How software pricing changes in an AI world, and why Sonar prices by lines of code analyzed

A line worth saving:

“Define acceptable risk as opposed to no risk.”

Pro Tips you can steal

• Write down what you want AI to achieve, the steps to get there, and the metric you will use to verify outcomes.

• For code generation, scan and review before shipping, treat AI output like a draft, not a final answer.

• Set clear rules for what is allowed with AI inside the company, then iterate as you learn.

Call to Action

If you want more conversations like this on software leadership, AI governance, and building real impact, follow The Tech Trek and subscribe on your favorite podcast app. If someone on your team is wrestling with AI rollout or developer productivity, share this episode with them.

Gregg Altschul, Vice President of Technology at FanDuel, shares a clear and practical look at how leaders can create real alignment across personal, team, and company goals. He explains why transparency drives trust, how to build a path for growth at every level, and why the best managers help people pursue their long term North Star while still delivering for the business. This is a thoughtful and modern blueprint for tech leadership and team development.

Key Takeaways

Teams move faster when the company goal is translated into a simple set of objectives that every level can understand and act on.

Transparency is the anchor for healthy goal setting and creates the space for honest conversations about career direction.

Managers should encourage long term North Star thinking since it keeps people growing even after short term milestones are reached.

Succession planning should be an active part of how teams operate so progress never depends on a single person.

People can stay committed to their work even if they have long term plans outside the company, and supporting those plans often improves retention.

Timestamped Highlights

02:19 How top level business goals get distilled into specific team and personal goals that engineers can act on.

04:57 The role of transparency in helping teams understand the why behind each objective.

07:34 Helping ICs tie personal development to broader company needs while still honoring their ambitions.

09:28 Creating a safe environment for honest career conversations in a world of hybrid and remote work.

15:14 Why knowing a person’s long term plans makes succession planning easier for everyone.

17:45 How Gregg works with his own manager on growth even when the title ladder narrows at the VP level.

A standout idea from Gregg

“As long as you have a North Star you will grow. Whether you ever reach the exact role you picture is not really the point. The point is growth.”

Call to action

If this conversation helped you rethink how goals work inside your team, share it with a colleague who will appreciate it. Follow the show so you never miss new episodes and connect with me on LinkedIn for more conversations with leaders shaping the future of engineering and data.

Ken Ringdahl, CTO at Emburse, joins The Tech Trek to share what it really looks like to grow from engineer to CTO without losing your love for building. He talks about staying close to the code while leading a three hundred person org, how he learned the business side on the job instead of through an MBA, and why curiosity is still his strongest tool. If you are an engineer who cares about leadership, AI, and long term impact, this one will hit close to home.

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Key takeaways

The best engineering leaders stay technical for as long as they can, then pick their spots to lean in where the business needs them most.

You can learn the business side on the job by raising your hand for cross functional work and building real relationships with sales, finance, and product leaders.

Curiosity is a career advantage, both in technology and in leadership, because the quality of your questions shapes the quality of your decisions.

A practical AI strategy comes from listening to customers, partners, and internal experts, then translating that into focused product bets instead of chasing shiny tools.

Do not rush into management just for the title, a deep foundation as an engineer will make every future leadership decision stronger.

Timestamped highlights

00:38 Ken explains what Emburse does and how modern spend management lives at the intersection of software, data, and finance.

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01:30 How he balances being an engineer at heart with the reality of leading many teams and products as CTO.

03:41 Ken reflects on missing his coding days, what he still tinkers with, and why he chose the bridge role between tech and business.

08:32 Learning leadership without an MBA, creating your own opportunities, and attaching yourself to people you can learn from across the company.

14:58 How he stays smart on AI through office hours, internal experts, cloud partners, customers, and investor networks.

21:22 His biggest advice for engineers who want to move into leadership and why he actually went back to a more hands on role before moving up again.

One line that stayed with me

“Even if you want to be a leader, do not rush it. Do not go so fast that you do not get that foundation.”

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Practical moves for your own career

Stay technical as long as you can, then choose a few focus areas such as architecture, AI strategy, or cloud patterns where you can still go deep.

Use curiosity as your main tool, ask simple but sharp questions of finance, sales, and customers so you see how technology really creates value.

Look for chances to run cross functional projects early in your career so that by the time you step into leadership, you already understand how the wider business works.

Treat partners, customers, and internal experts as an extended brain trust, especially when you are trying to shape an AI and platform strategy.

Listen and stay connected

If this episode helped you think differently about your own path from engineer to leader, follow The Tech Trek, leave a rating on your favorite podcast app, and share it with one person on your team. To keep the conversation going, connect with Ken on LinkedIn and find me there as well for more stories from leaders who are building real impact with technology.

Karan Talati, cofounder and CEO at First Resonance, joins me to unpack what modern manufacturing really looks like inside factories that build rockets, drones, reactors, and other complex hardware. We dig into why only a small slice of factories run on real systems today, what a true factory operating system unlocks, and how that connects directly to national security and the AI boom.

If you care about where all of this new compute, energy, and defense hardware will actually come from, this conversation gives you a clear view of the stack, the gaps, and the opportunity.

Key takeaways

• Only a small fraction of factories in the United States use a manufacturing execution system, which leaves a huge gap between legacy on prem tools, paper processes, and generic workflow apps that were never built for hardware work

• Cloud infrastructure and open interfaces now make it possible to deploy a purpose built factory operating system at a cost and speed that works for both fast moving startups and long standing suppliers

• Reindustrialization does not mean bringing every product back onshore, it means being deliberate about the layers of manufacturing that matter most for national security, chips, optics, and other high value components

• The real foundation for modern manufacturing is talent, there is a major chance to re skill people into highly technical, well paid roles in aerospace, semiconductors, energy, and more

• AI and agent style workflows will sit across design, manufacturing, and field operations so that hardware teams can close feedback loops, shorten timelines, and make better decisions with the data they already generate

Timestamped highlights

[00:40] Karan explains what First Resonance does and why he calls it a factory operating system for complex industries like aerospace, defense, energy, and autonomy

[01:55] How we ended up with only about fifteen percent of factories running on an MES, and why most hardware work still lives on paper, spreadsheets, and ad hoc tools

[06:49] A clear walkthrough of how offshoring looked like a rational path for decades, and why it created hidden risk across chips, optics, and other critical components

[11:46] Which parts of manufacturing should come back onshore, why you do not want everything local, and how workforce strategy fits into the new industrial map

[16:35] What a horizontal stack across design, factory systems, test, and field data can look like, and how AI agents can keep teams in sync across that stack

[23:02] The real timelines of hardware in the age of AI, why software is speeding up physical development, and why examples like SpaceX and TSMC matter for the next decade

A line that stayed with me

“Hardware and software are not separate worlds, they are one system that is now converging faster than most people realize.”

Practical moves for tech leaders

• Map your current manufacturing and hardware workflows, even if you are at a software first company, find the paper, spreadsheets, and disconnected tools that support anything physical you ship

• Look for one or two places where a factory operating system or modern MES could remove handoffs, for example design changes that take weeks to reach the line or test data that never feeds back into engineering

• Treat manufacturing careers as part of your talent strategy, help your teams see these roles as high skill and high impact, not as a side track

Call to action

If this episode gave you a clearer view of how hardware, AI, and national security tie together, share it with one other person who should be thinking about the factory side of their roadmap.

Follow and subscribe to The Tech Trek so you never miss deep dives like this, and connect with me on LinkedIn if you want more conversations at the edge of data, engineering, and real world impact.

Michael Marmo, founder and chief executive of CurbWaste, joins The Tech Trek to share how he went from catching fastballs in Europe to building software that runs the daily work of waste haulers. We walk through the very human side of leaving a sports identity, starting at the bottom in a family waste business, and finally asking a simple question about founding a company. Why not me

If you are sitting inside an industry and quietly seeing the gaps that no product seems to solve, this conversation is a playbook in how to turn that insider view into a real business, even if you do not come from a traditional tech background.

Key takeaways

• Identity can change, but the work habits that made you good at sports or any craft can transfer directly into building a company, especially persistence, dealing with failure, and showing up every day

• You do not have to love a specific activity forever, you can follow the deeper thread underneath it, like merit, teamwork, and visible impact, and find those same traits in a very different industry

• Deep time inside an industry lets you see painful, repeatable problems, and that is often a better seed for a product business than starting with a clever idea and pivoting until something sticks

• A clear why for the product and a clear why you are the person to build it are not nice to have, they are what convince customers, hires, and investors to follow you when things get hard

• Great founders do not pretend to be good at everything, they are honest about what they do not know, learn just enough to make good calls in product, engineering, and go to market, and then surround themselves with people who fill the gaps

Timestamped highlights

00:32 Michael explains what CurbWaste does and how it runs a hauler business from first customer contact through billing

01:21 From college baseball and pro teams in Europe to the first job in media and tech sales, and the identity shock that came with that change

06:27 What it really felt like when the game ended, why mens leagues did not scratch the itch, and how that led to a quiet reset in the working world

09:11 Starting at the bottom in a family recycling center, discovering a love for the waste industry, and why it felt like a merit based team environment

15:24 Walking the floor at Waste Expo, not finding the software he needed, deciding to fund and build his own tools, and seeing other haulers facing the same problems

19:40 The moment hearing the Yelp founder speak turned into a personal question, why not me, and how that idea of trying anyway shapes the way he thinks about founding today

A line that stayed with me

“At the end of the day he tried. He had an idea and he acted on it and pursued it. That really resonated. I was like, why not me”

Practical notes for future founders

• Before you write any code or quit your job, write down why this problem matters, why it matters now, and why you are willing to keep going when it stops being fun

• If your first answer to why is only about money, keep digging until you find something that still feels true on a hard day, because you will have a lot of those

• Use your current role as a live lab, list the moments that feel broken, expensive, or slow, and ask which of those could actually support a business if you solved them well

• Be direct with yourself about weak spots, whether that is product, tech, or selling, then build a basic understanding and lean on people who are strong where you are not

Call to action

If you enjoy stories that get inside how real founders make the leap from operator to builder, follow The Tech Trek in your favorite podcast app and share this episode with someone who is quietly thinking about starting something of their own.

Jason Ash, Chief of Data at Symetra, joins the show to unpack how a mid sized insurer is rebuilding its data stack and culture so business and technology actually pull in the same direction. He shares how his team brings actuaries, product leaders, and engineers into one data platform, and why opening that platform to non technical contributors has been a turning point. If you work in a regulated industry and are trying to move faster with data, this conversation gives you a very practical view of what it takes.

Key takeaways

• Business and tech only work when they share context and trust

Jason has sat in both seats, first as an actuary and now as a data and engineering leader. That dual background helps him translate between risk, regulation, and modern data practices, and it shapes how he frames projects around shared business outcomes rather than tools.

• Put data leaders inside business line leadership, not on the outside

Several of Jason’s managers sit on the leadership teams for Symetra’s life, retirement, and group benefits divisions. They hear priorities and constraints at the same time as product and distribution leaders, which lets them frame data as a value add for new products instead of a back office cost.

• Treat the warehouse as a shared product and measure contributors, not just tables

Symetra’s dbt based warehouse started with about five contributors. Over three years they grew that to more than sixty, and half of those people sit outside the core data team. Business users learn to contribute SQL, documentation, and domain knowledge directly into the repo, which spreads ownership and reduces bottlenecks.

• Shift stakeholders away from big bang launches to steady delivery

Jason pushes his teams to think like software engineers. Rather than promising a perfect data product on a single date, they deliver an early slice of data, have partners use it right away, collect feedback, and improve every month. That builds trust and avoids the usual disappointment that comes with one big release.

• Use maturity as a guide for where to invest

Early on, his group picked a few strong champions who were willing to accept slower delivery in exchange for building real infrastructure. Now that the platform and practices are in place, the focus is on scale, reuse, and getting more people to build on the same foundation, including as AI capabilities start to reshape the work.

Timestamped highlights

00:53 Jason explains what Symetra actually does and how their product mix makes data work more complex than the company size might suggest

02:19 From actuary to Chief of Data, and what sitting on both sides of the fence taught him about business and technology expectations

08:08 Why mixing data engineers, data scientists, actuaries, and analysts on the same problems leads to stronger solutions than any single discipline alone

13:44 How embedding data leaders into each business division’s leadership group changed when and how data enters product discussions

16:38 The dbt story at Symetra, and how more than sixty people across the company now contribute directly to the shared data warehouse

26:22 Moving away from big bang data launches and setting expectations around early value, continuous feedback, and ongoing quality improvements

32:06 The tension between safety and speed as AI advances, and what Jason worries about most for established insurers that move too slowly

Practical moves you can steal

• Put data leaders on business line leadership teams so they hear priorities and constraints in real time, not after the roadmap is set

• Track how many unique people contribute to your data warehouse and make that a visible success metric across the company

Stay connected

If this episode helped you think differently about data leadership in regulated industries, share it with a colleague who owns product, data, or actuarial work.

Chris Morgan, VP of Data Science at Lincoln Financial Group, joins me to unpack what a real data culture looks like inside a complex, highly regulated business that has policies on the books for decades. We talk about how to turn Gen AI buzz into real value, why governance and quality suddenly matter to everyone, and how to tackle data technical debt without stalling delivery.

Chris shares concrete ways he finds champions in the business, balances centralized and federated models, and keeps stakeholders excited about the future while he quietly fixes the messy data foundation underneath it all.

Key takeaways

Data culture is less about dashboards and more about curiosity, repeatable processes, and raising the analytical watermark across the company, not just in the data team.

The teams that will win with Gen AI are the ones that can safely connect proprietary data to these models, which demands strong governance, clear definitions, and shared standards.

A blended model works best for scaling data work, where a central function sets guardrails and standards while domain teams stay close to the business and own local decisions.

Paying down technical debt works when it is framed in business terms, tied to revenue and risk, and treated as a regular slice of capacity instead of a one time side project.

Education is now part of the job for data leaders, from internal road shows on Gen AI to simple stories that explain why foundational data work matters before you can ship shiny tools.

Timestamped highlights

00:04 Setting the stage Chris explains his role at Lincoln Financial and how data science supports life and annuity products that can live for decades.

03:33 The Cobb salad story A simple grocery store analogy that makes data standards and shared definitions instantly clear to non technical stakeholders.

06:06 Finding the right champions Why Chris prefers curious partners who will invest time with the data team over senior leaders who just want results without changing behavior.

08:33 Governance as Gen AI fuel How regulatory pressure and the need to trust what goes into models are pushing data governance and quality into the spotlight.

11:11 A practical way to attack data technical debt How Chris decides what to fix first, and why he tries to reserve a steady slice of team time for cleanup so progress is visible and sustainable.

17:44 Managing Gen AI expectations From road shows to constant communication, Chris shares how he keeps enthusiasm high while also being honest about the timeline and effort.

One line that sums it up

“These generative models are going to become a commodity and what will separate companies is who can take the most advantage of their proprietary data.”

Practical playbook

Start small with data culture by picking one engaged business partner, one problem, and one outcome you can measure clearly.

Reserve a consistent portion of team capacity for technical debt, even if it is only a small percentage at first, and make the tradeoffs visible.

Use stories, analogies, and simple rules of the road so stakeholders can understand how data systems work without becoming experts in the tech.

Call to action

If this conversation helped you think differently about data culture and Gen AI inside your company, follow the show and leave a rating so more engineering and data leaders can find it. To keep the discussion going, connect with me on LinkedIn and share how your team is tackling data culture and technical debt right now.