0:03 Cloud engineer versus data engineer.
0:05 Which career path should you choose?
0:08 We've seen non-stop rounds of tech
0:11 layoffs and fear is spreading across the
0:13 industry. Most of these layoffs
0:16 companies claim are because of AI and
0:19 big cos like Sam Ottoman keep saying AI
0:22 will eventually replace jobs non-stop.
0:24 But here's the truth. Both cloud
0:26 engineers and data engineers are the
0:29 ones who actually make AI possible. Data
0:32 engineers create usable data to feed AI.
0:35 Cloud engineers build an infrastructure
0:38 that allows AI to run live at scale. So
0:41 what's the difference? Which career path
0:44 fits best for you? And most importantly,
0:47 what separates those who thrive and stay
0:49 in high demand from it pros who risk
0:53 becoming outdated like legacy code in
0:55 old citizens. Hi, I'm Lois Roth, a
0:58 senior cloud engineer with over a decade
1:01 of experience in tech and mentor helping
1:03 professionals futureproof their careers.
1:05 And by the end of this video, I will
1:09 show you exactly which path to choose
1:11 based on your goals and today's market
1:14 reality. Before we break down the key
1:16 differences, let's start with the
1:19 similarities. Both roles rely heavily on
1:22 cloud platforms. Whether it's Microsoft
1:26 Azure, Google Cloud or AWS, you need to
1:29 choose one to specialize in. Inside
1:31 these platforms, there are hundreds of
1:34 services and past tools. Somes are
1:37 uniques to each role while others are
1:40 shared but used with different goals and
1:42 at different levels of depth. So let's
1:45 start by looking at what a cloud
1:48 engineer does. Cloud powers almost
1:50 everything you touch online today.
1:52 Whether you are chatting with chatty
1:55 GTP, scrolling YouTube, or streaming
1:58 Netflix, it's all running carefully
2:00 designed cloud infrastructure hidden
2:02 behind the scenes. Before the cloud,
2:05 companies relied on their own servers
2:08 stacked in data centers. It was
2:10 expensive, is slow to adapt, and when
2:13 the main changed, servers crashed. If
2:15 traffic dropped, money was wasted on
2:18 idle hardware. The rise of the public
2:20 cloud flipped that model on its head.
2:23 Now business can simply rent computing
2:26 resources from platforms like Asure, AWS
2:29 or Google Cloud. Need more capacity? You
2:32 scale instantly. Less demand, scale down
2:35 and stop paying. It's efficient, it's
2:38 flexible, and reshaped the entire tech
2:40 industry. And the people who make that
2:42 happen are cloud engineers. Every
2:45 business from government to banking and
2:48 to health care is racing to the cloud.
2:52 Yet more than 70% of enterprises still
2:55 depend on legacy systems and over 8%
2:58 operate in hybrid model. This complex
3:00 challenge requires skilled cloud
3:03 engineers to design secure, scalable and
3:06 seamless solutions. So what do cloud
3:09 engineers actually do daytoday? As a
3:11 cloud engineer, you will work with Azure
3:14 or other cloud platforms from the ground
3:16 up. You will manage virtual machines,
3:18 not just turning them on, but
3:21 understanding how they connect, how to
3:23 secure them, and how to keep them
3:25 resilient when failures happen and
3:27 demand increases. You will work with
3:29 blob storage. It may look simple, but is
3:32 the backbone of storing unstructured
3:34 data at scale. From files to backups,
3:36 you will manage containers, control
3:39 access, ensure data stay secures,
3:41 durable and highly available. You will
3:44 design virtual networks. This is where
3:46 everything connects. Subinets, routing,
3:48 and security rules to make systems
3:51 communicating safely, efficiently, and
3:53 with high availability. You will run
3:55 security and identity management
3:58 defining access with rolebased access
4:01 control enforcing zero trust principles
4:03 and safeguarding work of loads across
4:06 the cloud. Once the foundation is solid
4:08 you will tie everything together with
4:10 infrastructures as a code using tools
4:12 like Azure bicep or terraform to
4:14 automate and keep environment
4:16 consistent. From there, you will build
4:19 pipelines with Azure DevOps or GitHub
4:21 actions to push applications into
4:24 production quickly and reliably. But the
4:26 role goes beyond technical work. Cloud
4:28 engineers collaborate across teams,
4:31 align with leadership, and translate
4:34 business needs into practical, scalable
4:36 solutions. They sit at the intersection
4:39 of technology and strategy. That unique
4:42 mix of technical expertise and
4:44 communication makes the role
4:47 irreplaceable for any modern business
4:49 and salaries reflect that. Cloud
4:51 engineer salaries typically range from $110,000
4:53 $110,000
4:56 to 106 with senior engineers and
4:59 architect roles often reaching $170,000
5:03 to $200,000 at the top end. That's why
5:05 cloud engineering is considering one of
5:08 the most futureproof careers in tech.
5:10 Now let's look at the other side. Data
5:13 engineers. Every AI model, every
5:16 dashboard, every business report you
5:19 ever seen depends on one thing. Clean,
5:22 reliable data. Without data, there is
5:24 nothing to analyze. Without structure,
5:26 there is nothing to build on. That's
5:29 where data engineers come in. Instead of
5:31 managing servers and infrastructure,
5:34 your focus is on data pipelines. You
5:38 take raw massive data from logs, apps,
5:40 customer transactions and clean it up,
5:42 joining it with other resources and
5:45 making it consistent. You design systems
5:48 that move this data reliably whether
5:51 through realtime streams or large scale
5:53 batch jobs. You build and manage
5:56 databases, warehouses and lakes, places
5:59 where massive volumes of data can be
6:01 stored. Even generative AI relies on
6:03 this work. Splitting documents,
6:06 embedding, storing vectors. It all comes
6:10 down to data pipelines. Agent AI, same
6:13 story. Tools must connect, data must be
6:16 clean, logs must be tracked. Even
6:18 dashboards showing live business metrics
6:20 depends on data engineering to ingest
6:23 strings, aggregate fast, and stay within
6:26 service level agreements. The role is
6:28 highly involved with numbers, often
6:30 requires advanced coding skills, and
6:32 appeals to people who enjoy working with
6:35 ambiguity and solving data problems at
6:38 scale. Data engineers also can earn
6:41 competitive six-figure sellers, usually
6:44 around $100,000 to $150,000.
6:47 So, how do these two rows compare? Both
6:49 are futureproof. Both are well paid.
6:52 Both are essential for AI modern
6:54 business. Cloud engineers build the
6:56 infrastructure, the systems, the
6:59 networks, the security that everything
7:02 runs aligned with business goals. Data
7:04 engineers build the pipelines, the
7:06 cleaning, the storage to make
7:08 information usable so business can make
7:11 smarter decisions. Take Azure blob
7:14 storage. A cloud engineer uses it mainly
7:17 for hosting application files, VM discs
7:20 or backups, making sure it's secure and
7:22 cost efficient. A data engineer on the
7:25 other hand relies on its as a data lake
7:28 dumping raw data sets feeding them into
7:30 pipelines or machine learning models. Or
7:32 look at Azure SQL database. A cloud
7:35 engineer might provision configure
7:37 secured and manage backup. A data
7:40 engineer uses SQL daily cleaning joining
7:42 and transforming data into something
7:45 usable for analysis or reporting. Now
7:47 consider Azure datab bricks and
7:49 Microsoft fabric. A cloud engineer sets
7:52 up environment, manage networking and
7:55 integrates identity. Identity engineer
7:57 spends their time inside running
8:00 analytics and turning raw inputs into
8:01 insights or features for machine
8:04 learning. So even though both roles
8:06 touch cloud platforms, when you compare
8:08 the two, the difference becomes clear.
8:11 Data engineering is more analytical. If
8:13 you enjoy problem solving at the data
8:15 level, coding and like the idea of
8:17 cleaning and shaping information
8:19 insights, data could be a good fit.
8:21 Cloud engineering on the other hand is
8:23 about building infrastructure. It's less
8:26 about complex algorithms and more about
8:28 systems thinking. You still code, but
8:31 it's not as heavy as in data pipelines.
8:33 If you prefer building environments,
8:35 managing systems and focusing on
8:37 scalability, security and even
8:40 interacting with stakeholders, cloud is
8:42 better choice. Now, if you're still
8:45 trying to decide which one to pursue,
8:47 here's my recommendation. Start with
8:49 cloud engineering. Here's why. Every
8:52 company needs the cloud. Whether or not
8:54 they adopt AI, whether or not they build
8:56 advanced data systems, cloud
8:58 infrastructure is backbone. the demand
9:00 is broader and the skills transfers
9:02 everywhere. Then once you're confident
9:05 in cloud, you can always add data
9:08 engineering on top to upskill. That
9:10 combination makes you extremely
9:12 valuable. The kind of engineer companies
9:14 compete to hire. And if you want a
9:17 proven road map to get there, check out
9:20 the link in the description. Enjoy my
9:22 program, the cloud career launchpad.
9:24 It's packed with everything you need to
9:26 go from zero to confident job ready
9:29 cloud engineer. From cloud fundamentals
9:32 to real world projects to interview and
9:34 positioning strategies that actually
9:36 work to the soft skills required
9:39 specifically for cloud engineers. So if
9:41 you are aiming for a six-figure career
9:43 in tech, this could be your chance. And
9:46 before you go, don't forget to hit like,
9:47 subscribe, and share this video with a
9:50 friend who needs to hear it. I will see
