Ethan Aaron

Portable

At Portable, we help data teams connect their long tail business applications to their data warehouse with no code. 

1000+ prebuilt integrations and we'll build new connectors on-demand for clients in hours or days.

Founder, CEO

LiveRamp

After LiveRamp sold its parent company, Acxiom Marketing Solutions, to IPG for $2.3bn, I joined the strategy and corporate development team to identify partnerships and strategic M&A opportunities in the data ecosystem.

Director of Strategy & Corporate Development

Identified the opportunity to centralize all analytics functions at LiveRamp into one team and technology stack. Evaluated and managed ELT / ETL tools, transformation capabilities, warehouse environments, and visualization tools across business units. Came up with a strategic plan to migrate to single vision and centralized tech stack.

Head of Business Intelligence

Driver of everything Business Intelligence at LiveRamp - our holistic vision, strategy, and execution across the company. More generally, focused on enhancing operational efficiency through cross-functional data, process improvements, and organizational changes to ensure the company can scale effectively.

Director of Operations & Business Intelligence

Product owner for LiveRamp's publisher offering.

Product Manager

Arbor (acquired by LiveRamp)

Enhancing Arbor's product offering by:

- Engaging publishers, partners, and internal stakeholders for product demand
- Determining product requirements and design
- Working with our engineering team to implement and build new products
- Assisting with technical account management and client integrations

Focused on developing Arbor's mobile data platform through:

- Client engagement and new business development
- Building collateral to help streamline the partner onboarding process
- Creating business intelligence and and external reporting dashboards utilizing SQL
- Technical account management and client integrations
- Analyzing client and internal data sets with bash and shell script

Business Development Manager

Goldman Sachs

Projects and Transactions Include:

- Portfolio Wide Energy Efficiency Initiative | LEED AP O+M
- Acquisition of 200 Unit Apartment Community
- Conducted Due Diligence on Senior Housing Real Estate Fund
- Developed Modular Analyst and Associate Training Program
- Creation of Real Estate Fund Database for Reporting and Analytics
- Automation and Analytics for Weekly Sourcing Pipeline

Real Estate Private Equity Analyst

Goldman Sachs - Real Estate Investment Group

Summer Analyst

Goldman Sachs - Private Equity Group

State Street

IT Intern

CEO @ Portable | 500+ Cloud-Hosted ELT Connectors

Founder of Portable. We help companies ingest data into their data warehouse or data lake with no-code. We're like a part of your data engineering team, but with 1400+ prebuilt integrations and lightning-fast development capabilities. Fixed pricing. Unlimited data volumes. Super-friendly support.

Who's hiring in data + AI right now?

Have an open role? Know of someone hiring?  Drop it below 👇

Happy Sunday!

Integration developer | Founder @ Portable | Fixed-price, reliable, ELT

The data professionals I know making 300k, 400k, 500k per year don't talk about SQL (or their data stack) when they ask for a raise or negotiate an offer at a new company.

They talk about the things that actually matter to the business and how they're the best person to solve those problems or capture those opportunities.

--

Your technical skills will only get you so far.

(Unless you work at Meta, Google, or OpenAI - those companies are in their own little bubble of R&D)

For a company called LinkedIn. They hate nothing more than when you have websites Linked In your posts.

Portable now integrates with Microsoft Teams. But in a way that you would NOT expect...

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Portable is known as one of the best data ingestion / ELT tools out there.

With no code, data teams can pull data from 1500+ tools and load it into their data warehouse or data lake.

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But now!

You can now run SQL queries against:
1. Snowflake
2. BigQuery
3. PostgreSQL
4. MySQL

And send the results as a channel message to Teams.

- Engineering releases can be summarized into a changelog for your marketing team to format and publish
- Manager updates (i.e. what did we do this week), can be pulled from GitHub or your ticketing system, summarized, and pinged to a channel
- High value customers, or customers at risk of churn, can be bubbled up to your sales team for targeted comms
- Data quality alerts can be flagged and sent to stakeholders to resolve (on their own, without the data team needing to do a thing).

--

Oh, I forgot to mention.

After you run the query, you can then send the data to openAI to summarize it / generate insights / or nicely format everything before posting to Teams.

More to come, but our new AI automation capabilities are 🔥🔥🔥. And we already support Slack as a destination.

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Our vision for the next 6 months?

Build prebuilt workflows for data teams that help you automate the grunt work you've inherited (as teams have been downsized in recent years).

We are going to automate all the stuff data teams hate doing, so you can finally get back to working on the fun stuff!

Curious to learn more? I have some time next week, and I'd love to give you a sneak peak at what we're building!

Congrats. Your 1 person data team was just tasked with the most important technical project in your company.

- No new resources
- Limited budget
- But don't worry, it's REALLY important to your executives

The project might be:

1. Create your company's first ever AI agent to automate comms

2. Build user-facing reporting that pulls in data from disparate data sources, generates novel insights, and exposes them to clients

3. Develop a suite of exec metrics your company will use to survive the next 12 months

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Here's where you should start.

1. Before doing anything... breathe

It's 2025, and you're not alone. If you're still working in data at this point, you've come to realize a few things. Teams are lean. Budgets are scarce. And business value is the most important thing to surviving. At the same time, take a step back, and know that you are the smartest person in the company at solving the problem you've been tasked with, and it just comes down to planning, execution, and communication.

2. Get to vivid image of success.

If you've been tasked with building an AI agent, ask 'what's a very specific workflow for a very specific person that this agent should be able to solve'. Make sure everyone is aligned on what this looks like. If the answers feel hand wavy, or too general, keep digging into what success looks like. Don't start building a solution until you know the exact output.

3. Focus on time to value, not scalability

Come up with a plan that will get you to the output as fast as possible. And make sure to prioritize speed over perfection. Self-service is a waste of your time. Streaming or real-time pipelines probably won't help. What you really need to figure out is how do you get value into the hands of business stakeholders as quickly as possible. Once you deliver value, you can ask for more resources or time to make things scalable.

4. Don't try and build everything from scratch.

Extra money and bonus headcount were sooo 2021. It's 2025 and no one has the luxury of extra resources to build stuff they shouldn't be building. When you move into execution mode, find the best experts you can to accelerate your plans. (If you need integration or AI workflow help, let me know - happy to walk you through how Portable can help). If you're considering things like: python, rolling your own [ insert whatever solution already exists ], or doing a long research project into any sort of landscape -- stop. Take a step back and find someone that's done it before.

5. When you create value, write it down

When you ship something valuable, make sure to get feedback from stakeholders on how much time it saved or money it generated. If you record your internal calls (I highly recommend doing this), make sure to save some clips of stakeholders describing how valuable your deliverable was. If you ever need more resources or more time, this will be critical.

6. Breathe

Resources are scarce. You're doing the best you can. Just keep shipping 🚀

🫡

Since founding Portable in 2020, I've never paid to sponsor anything before... Why?

Because I enjoyed the community building and relationships that come from hosting our own events, building our own relationships, and getting in the weeds personally.

I attend events all the time.

I host events every month (low-key happy hours have been around for 3+ years 🤯).

And I host really big events about once a year (at Snowflake summit a couple times).

But when Aaron Wilkerson described the in-person conference he was building with other great members of the Detroit data community, it was an event I wanted to get behind.

Not because of logos or brand awareness.

But because it's a genuine local data community, creating an event for real data practitioners.

We're excited to sponsor.
I'm excited to speak alongside Brendan Fuller.
And I'm really looking forward to attending.

If you haven't already:
1. Registered
2. Booked a flight
3. Made plans to attend

Do it now.

I'll see you in Detroit in a couple weeks!

Takeaways from my data conversations this week:

- Fivetran is still expensive (and renewals are crazier than ever)
- Data teams are understaffed
- Any help from actual human beings is really valuable right now (vs just telling people to deal with a product and error logs)

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Find yourself on an understaffed data team and looking for a new ELT partner?

Want to work with people that truly feel like they’re part of your team?

Get in touch. I’d love to walk you through how we help clients at Portable!

CEO: We need AI to streamline our support processes. We need it now.
Data Team: But first we need to get our data stack in order.
Me: That's stupid. No you don't. You should really listen to what your execs actually care about.

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A lot of data teams are trying to use AI as a justification for more technical spend on their bloated 'modern data stack'
- More data ingestion
- More SQL queries
- Fancy observability tools
- Overpriced compute
- A semantic layer
- Self-service dashboarding tools

It's as if they hear what the execs are asking for, and immediately translate it into:

'Oh, this is a great excuse for me to keep doing what I've been doing'

Instead of stepping back and questioning whether they're even working on the right things.

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If your execs are asking your team to use AI to automate real business workflows.

There is a VERY good chance you can automate those workflows without data ever touching a data warehouse. i.e.
- Without data observability.
- Without a semantic layer.
- Without some crazy DAG of model builds.
- Without streaming + CDC pipelines from your database

And if that's the case.

Just do it.

Automate the workflow.

Solve the problem.

Don't pretend like you understand what the execs want better than they do.

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On the other hand.

There are some situations where the AI workflows depend on views of data that can only be created by running large joins across disparate systems -- i.e. information from a CRM, a database, a ticketing platform.

In those situations, your data warehouse can be great as a source for your AI pipelines.

But again.

The execs aren't asking you for a perfect data stack.

They don't care how you get it done.

They're asking you to solve a problem.

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Solve the problem.

Don't forget to register for Data in the D (November 8th)! It's going to be a blast.

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