IoT and Embedded Systems Skills for Connected Device Work
By vaayaa Editorial Team · Subject panel: Hiroshi Tanaka (PhD, Computer Science), Carlos García (Master's, Data Science)
IoT and embedded systems work is about building devices that sense, decide and communicate under strict limits of power, memory and time. The eight courses cover the full stack, from firmware on a microcontroller to edge and fog layers that process data before it reaches the cloud.
Key takeaways
- Embedded systems engineering sits at the core; IoT adds connectivity and cloud integration on top.
- Edge and fog computing move processing closer to devices to cut latency and network dependence.
- Firmware and microcontroller skills are the entry point for most embedded roles.
- FPGA work adds reconfigurable hardware acceleration for demanding signal and control tasks.
- Job listings ask for C, Embedded C, RTOS knowledge and protocols such as MQTT, SPI, I2C and CAN.
- A portfolio of end-to-end device projects matters more than certificates alone.
Connected products share one architecture: a small computer reads sensors, runs logic, and sends selected data onward. The hard part is doing that reliably with limited memory, limited power and hard timing limits. That constraint is what separates embedded work from ordinary software work.
The eight courses in this group describe one career ladder. At the bottom sits the microcontroller and the firmware that runs on it. Above that sits the embedded system as a whole, with its hardware and software integration. At the top sit the network layers: IoT connectivity, then edge and fog computing, which decide where data gets processed. FPGA programming runs alongside as a route to custom hardware acceleration.
This discourse explains what each skill is, where it is used, which tools and roles it involves, and how the pieces fit into a learning path you can follow without taking every course. The Edge Computing Systems course covers one more part of this theme.
IoT and edge computing turn devices into data systems
The Internet of Things is the practice of connecting physical devices to networks so they can report data and act on commands. An IoT solution spans sensors, a gateway, a cloud platform and often a mobile app. Job listings for IoT engineers ask for sensor calibration, motor driver circuits, PCB tools and cloud platforms alongside Embedded C [2]. The Internet of Things course covers this full chain from device to dashboard.
Edge computing moves part of that processing onto or near the device. The reason is practical: sending every reading to a distant data center adds delay and depends on a working network link. Running logic locally gives faster responses and keeps working when connectivity drops. SlashData estimates that 29 percent of professional developers, about 11 million people, build or integrate functionality on projects that target direct implementation into edge devices [14].
Typical tasks and roles
- IoT engineer: integrates sensors, microcontrollers and gateways, then connects them to a cloud platform [4].
- IoT solutions engineer: designs provisioning, remote configuration and over-the-air firmware updates across a device fleet [4].
- Edge engineer: decides which logic runs on the device and which runs in the cloud, then tunes memory and power use.
- IoT architect: sets protocol choices and system integration patterns across a product line [5].
Fact An analysis of edge and cloud employment projects a net 690,000 jobs created globally across edge computing and related fields, alongside a 2.3 million talent shortfall and a 56 percent salary premium for scarce skills [10].
Embedded systems skills appear in far more C++ postings than IoT or FPGA skills in this market sample, which suggests the device level is the largest hiring pool.
Source: IT Jobs Watch [12]
View chart data
| Embedded systems | 9.01% |
| Robotics | 5.57% |
| FPGA | 4.28% |
| Embedded Linux | 3.5% |
| IoT | 3.05% |
| Device driver | 2.79% |
| VHDL | 2.27% |
Fog computing extends the cloud toward the data source
Fog computing is a layer between the device and the cloud. It puts storage, computation and networking in local nodes such as gateways or on-premise servers, so data can be filtered, aggregated and acted on close to where it is produced. The Fog Computing Technology course treats fog as a distributed computing design problem rather than a single product.
The distinction from edge is mainly one of scale and placement. Edge processing usually happens on or beside the device. Fog covers a wider local tier that can serve many devices and coordinate between them. In practice, engineers choose among three placements: on the device, in a local fog node, or in the cloud. Each choice trades latency, bandwidth, cost and management effort.
Skills that carry across all three layers
- Message protocols such as MQTT and CoAP, which appear in IoT and fog job listings [5].
- Data modeling and filtering, so only useful readings travel upward.
- Reliability design: retries, buffering during outages and secure device identity.
- Basic orchestration and monitoring of distributed services.
Opinion For most product teams, the choice between edge and fog matters less than the discipline of deciding, in writing, which computation must happen locally and why. That decision drives cost and reliability more than any specific platform.
Experts scored data, network and testing profiles above pure firmware roles, which points to hybrid skills as a differentiator.
Source: ngiot.eu [13]
View chart data
| Data analytics specialists | 8.7 score |
| IoT network engineers | 8.3 score |
| IoT testing engineers | 8.3 score |
| Automation and systems integration | 7.3 score |
| Cybersecurity engineers | 6.7 score |
| IoT hardware designers | 6.3 score |
| Embedded firmware developers | 5.3 score |
Embedded software and systems development build the device itself
Embedded software development is writing code that runs on a dedicated computer inside a product, with tight limits on memory, processing and power. Common requirements in job listings include Embedded C, real-time operating systems such as FreeRTOS, ThreadX or Zephyr, interrupt handling and inter-task communication [3]. The Embedded Software Development course focuses on writing reliable firmware within those constraints.
Embedded systems development widens the view to hardware and software together: schematic reading, peripheral selection, board bring-up and validation. The Embedded Systems Development course frames this as a systematic design skill, which matches how employers describe the role. One listing for an embedded systems engineer asks for single-board computer experience, Embedded C and Python, plus a debugging mindset with oscilloscopes and logic analyzers [2].
What the daily work looks like
- Read the requirements and the hardware datasheet to map pins, buses and timing.
- Write drivers for sensors and peripherals over I2C, SPI or UART.
- Structure the application around tasks, interrupts and a scheduler.
- Debug with JTAG or SWD, a logic analyzer and logged traces.
- Test on the bench, then validate behavior under fault conditions.
| Aspect | Firmware and microcontrollers | IoT and edge computing | FPGA and hardware acceleration | Basis |
|---|---|---|---|---|
| Core output | Reliable device control code | Connected data pipelines and local processing | Custom logic mapped onto configurable hardware | Opinion |
| Main languages | C and Embedded C [3] | C, Python and platform SDKs [2] | Verilog and VHDL [12] | Fact |
| Key methods | RTOS scheduling, interrupts, watchdogs [3] | Protocol integration, provisioning, remote updates [4] | RTL design, timing closure, performance tuning [1] | Fact |
| Typical tools | JTAG, logic analyzer, protocol analyzer [3] | MQTT brokers, cloud IoT platforms [5] | Synthesis and simulation toolchains | Fact |
| Where it sits | On the device | Between device and cloud | Beside or instead of a processor | Opinion |
| Advertised share in one market | Embedded systems about 9 percent of C++ listings [12] | IoT about 3 percent of C++ listings [12] | FPGA about 4.3 percent of C++ listings [12] | Fact |
| Entry difficulty | Moderate, needs hardware familiarity | Moderate, needs networking basics | Higher, needs digital design grounding | Opinion |
Firmware and microcontroller programming are the entry point
Firmware engineering is the craft of making a microcontroller control hardware predictably. It covers bootloaders, secure updates, encryption and power management, alongside ordinary application logic. A firmware role listing asks for STM32, ESP32 and NXP parts, MISRA-C coding discipline, and experience with CAN, LIN, Modbus, BLE and Wi-Fi [3]. The Firmware Engineering course addresses this hardware-to-software bridge directly.
Microcontroller programming is the narrower, practical skill underneath: configuring clocks and timers, handling interrupts, using a vendor hardware abstraction layer, and managing memory without an operating system. Many developers arrive from high-level software and find the constrained environment unfamiliar. The Microcontroller Programming course is aimed at that transition.
Employers often split these roles into tracks. A career overview describes core firmware and RTOS engineers as the backbone of the field, with separate tracks for embedded Linux, connectivity, automotive safety and security [1]. That split means a learner can enter through firmware and later specialize.
Estimate The same career overview identifies security-focused embedded work, including secure boot and encrypted updates, as its own track [1]. This suggests that security knowledge added to firmware skills widens the roles a candidate can apply for.
FPGA programming adds reconfigurable hardware acceleration
An FPGA is a chip whose internal logic can be rewired after manufacture. Programming one means describing circuits in a hardware description language such as Verilog or VHDL, then mapping them onto configurable blocks. The work suits tasks that need deterministic timing or parallel processing beyond what a general processor can deliver at acceptable power. The FPGA Programming course covers this reconfigurable approach.
FPGA skills appear in demand data alongside embedded work. One UK job market analysis of C++ postings lists FPGA among related skills, cited in about 4.3 percent of the analyzed listings, and VHDL in about 2.3 percent [12]. Those are niche shares, which fits a specialty used where acceleration or custom interfaces are required.
Where FPGAs show up
- Signal processing in instrumentation, radio and imaging systems.
- Custom interfaces and protocol handling that must meet exact timing.
- Hardware acceleration next to a processor in a system on chip.
- Prototyping logic later moved into an application-specific chip.
A career overview groups FPGA, system on chip and hardware-software co-design into a single specialist track, listing RTL, accelerators and performance tuning as its skills [1]. That is a fair description: the value comes from moving computation into hardware where software is too slow.
How the main skill areas compare
The table below compares three broad areas using the language of real job listings and skills research. It is a guide to emphasis, not a ranking. Many engineers combine two areas, and the highest-value roles often sit between them.
Notice how the number of advertised roles differs by area. A UK skills analysis found embedded systems cited in about 9 percent of the analyzed C++ listings, against about 3 percent for IoT and 3.5 percent for embedded Linux [12]. Those shares describe one market's postings, not global demand, but they illustrate that core embedded work is the larger pool.
How to start learning embedded and IoT skills
Start with the device, not the cloud. A learner who can blink an LED, read a sensor over I2C and explain the timing has a foundation that every later layer depends on. From there, add an RTOS, then connectivity, then a processing layer.
- Learn C and Embedded C properly, including pointers, memory layout and bit manipulation.
- Pick one microcontroller family and learn its toolchain, debugger and datasheet.
- Add an RTOS and practice tasks, queues and interrupt handling.
- Connect the device with MQTT or BLE and build a small end-to-end project.
- Study edge or fog placement decisions for that project and document the trade-offs.
- Optionally explore FPGA tools if timing or acceleration problems interest you.
Assumption This pathway assumes roughly ten hours of study per week over several months, which is a planning assumption, not a measured outcome. Learners who already write software can usually move faster through the C stage.
Original analysis
vaayaa analysis: where the advertised demand sits
- Take the related skill shares from the UK C++ listing analysis: embedded systems 9.01 percent, FPGA 4.28 percent, embedded Linux 3.50 percent and IoT 3.05 percent [12].
- Add the device driver share of 2.79 percent, since driver work is part of embedded roles [12].
- Sum the device-level shares: 9.01 plus 2.79 equals 11.80 percent.
- Sum the connectivity and acceleration shares: 3.05 plus 4.28 plus 3.50 equals 10.83 percent.
- Compare the two totals to see which side of the stack appears more often in that sample.
Device-level skills appear slightly more often than connectivity and acceleration skills combined, at 11.80 percent against 10.83 percent. The gap is small, so the safer reading is that both sides matter, and a candidate who can work across them matches the hybrid profiles that employers describe [10]. This is a comparison of one market's listings, not a forecast.
A learning pathway from first microcontroller to connected system
- 1
Foundations
Learn C, memory layout and digital logic before touching a board.
- 2
Device control
Program one microcontroller family, driving pins, timers and interrupts.
- 3
Firmware structure
Add an RTOS and practice tasks, queues and safe fault handling.
- 4
Connectivity
Connect the device over a protocol such as MQTT or BLE and build a small pipeline.
- 5
Processing placement
Study edge and fog design choices and decide what runs locally.
- 6
Specialization
Branch into security, automotive, embedded Linux or FPGA acceleration.
Worked example
Sizing the data reduction from local averaging
- Count the sensors: 200 vibration sensors in one building.
- Count the readings: 50 per second each, so 10,000 readings per second in total.
- Multiply by payload size: 20 bytes per reading gives 200,000 bytes per second.
- Convert to a daily figure: 200,000 bytes times 86,400 seconds is about 17.3 gigabytes per day if all raw data is forwarded.
- Assume a local node computes a one-minute average per sensor and forwards alerts: that is 200 averaged values per minute instead of 600,000 raw readings per minute.
- Compare: the onward stream falls from 600,000 readings per minute to 200 averaged values plus alerts, a reduction of more than 99 percent in message count.
Local processing turns an unmanageable raw stream into a small status feed. The same arithmetic helps decide whether a fog node or cloud service is needed at all.
Pre-build checklist for a connected device project
- Write the timing and power limits for every function.
- Confirm the chosen microcontroller has the buses and memory the design needs.
- Define the update and recovery plan for firmware in the field.
- Decide which data stays local and document the reason.
- Select debugger and analysis tools before coding starts.
- Put the project under version control with a repeatable build.
Frequently asked questions
Do I need a degree to work in embedded systems?
Job listings commonly ask for a degree in electronics, electrical engineering or a related field, but they also stress demonstrated building ability. A portfolio of working projects, with code and documentation, is how candidates show that ability. Many listings accept related experience in place of a specific qualification.
Is IoT a separate career from embedded systems?
They overlap heavily. Embedded work covers the device and its firmware, while IoT work adds connectivity, cloud integration and fleet management. Many roles combine both, and listings for IoT engineers ask for embedded skills such as Embedded C, sensor integration and microcontroller experience alongside platform knowledge.
What is the difference between edge and fog computing?
Edge computing processes data on or immediately beside the device. Fog computing adds a local tier, such as a gateway or on-premise server, that serves many devices and can coordinate between them. Both reduce latency and network dependence compared with sending everything to a distant data center.
Which programming languages should I learn first?
C and Embedded C come first for device work, because most firmware and driver code is written in them. Python is a useful second language for scripting, testing and tooling, and listings often pair the two. For FPGA work, add Verilog or VHDL once the fundamentals are solid.
Are FPGA skills worth learning?
They are a specialty rather than a general requirement. FPGA appears in a small share of analyzed listings compared with embedded systems overall, so it is best treated as a second step. It pays off where timing, parallelism or custom interfaces matter, such as instrumentation, radio and imaging.
How do I show my skills without work experience?
Build complete devices rather than isolated exercises. A sensor node that reads data, applies local logic, connects over a protocol and survives a network outage demonstrates several skills at once. Publish the code, a short design note and the trade-offs you chose. Employers describe problem solvers with debugging evidence as the candidates they hire.
Conclusion
The eight courses describe one coherent field. Microcontroller programming and firmware engineering build the base, embedded software and systems development widen it to hardware integration, and IoT, edge and fog computing connect the device to the wider system. FPGA programming provides a specialist route into hardware acceleration.
For a learner, the practical order is device first, connectivity second, processing placement third. For a working engineer, the useful move is usually across the stack: a firmware developer who understands edge placement, or an IoT developer who can debug at the register level, is the hybrid profile employers describe.
References
- LinkedIn. Embedded System Career Paths and Job Skills.
- Shine. Embedded Systems Domain Jobs.
- Naukri.com. Embedded Firmware Engineer job listing.
- Naukri.com. IOT Solution Engineer job listing.
- Shine. Mqtt Jobs.
- Cutshort. Firmaware Engineer IOT job listing.
- Cutshort. Embedded Systems Intern job listing.
- Shine. System Utilities Jobs.
- Emertxe. Emertxe Placement Report.
- LinkedIn. Edge AI to Create 690K Jobs.
- Emertxe. Emertxe's Bulk Placement Run.
- IT Jobs Watch. C++ Job Trends, Salaries and Related Skills.
- ngiot.eu. Experts Consultation and Dialogues Report Version 2.
- SlashData. Rapid growth in edge AI developers and where the opportunity lies.