Open to work
Remote / hybrid in Europe
Real-time data systems
Junior Data Engineer
Junior data engineer who understands what happens when industrial systems fail.
Nine years spent maintaining critical water infrastructure shaped an approach centered on reliability, fast troubleshooting, and monitoring systems that help prevent failures before they become production problems.
Python
SQL
MQTT
Kafka learning path
Industrial IoT
Operational reliability
Explore
Jump to the sections that matter most.
Use these buttons to jump directly to proof, experience, skills, roadmap, or contact details.
Projects
Featured work
Production-facing industrial monitoring at Sonaca, built around live MQTT telemetry, 500ms latency, and real operational users.
The problem
Maintenance teams had to physically access machines to check sensor readings. Critical parameters were invisible until someone walked the floor, and anomalies were discovered too late.
What I built
A real-time monitoring system that brought machine telemetry to supervisor dashboards, with threshold alerts and historical SQL validation for operational decision-making.
The result
Supervisors could see production-floor health at a glance. Anomalies triggered instant visual alerts instead of delayed manual checks.
Before
Maintenance teams walked machines to check sensors.
After
Real-time dashboard with automatic alerts and validation.
Featured project
Real-time industrial monitoring at Sonaca
Designed and built a real-time data ingestion and monitoring application connecting to the Siemens BFC Gateway via MQTT to process live telemetry from industrial machines in an aerospace manufacturing environment.
The system processed machine data at sub-second update frequency (500ms latency), transforming JSON payloads into structured application entities and delivering real-time visibility through threshold-based alerts and SQL-backed historical validation.
Open technical details and GitHub activity
What I built
- MQTT subscription flow for continuous telemetry ingestion
- Asynchronous in-memory model for latest machine values
- Fallback logic to preserve last-known values during connection loss
- Threshold-based alerting using configurable machine parameters
- SQL validation against historian data for troubleshooting and comparison
- Role-based access patterns for operator and supervisory views
- Dashboard navigation by production area, machine, and sensor
Technical environment
MQTT
JSON
SQL
AVEVA Historian
Industrial IoT
Alert logic
Agile/Scrum
IEC-62443
Active on GitHub
Building in public, one proof point at a time.
GitHub is where architecture notes, portfolio projects, and public work are gradually turning into stronger case studies.
- Sonaca monitoring architecture and documentation
- Zoomcamp projects as they become portfolio-ready
- Consistent public progress instead of one-off demos
Portfolio direction
More deployable data projects are coming next.
This portfolio will keep expanding with public projects around orchestration, warehouse modeling, and streaming ingestion patterns as they become production-ready.
Experience
Relevant experience
Final academic project
Sonaca
Built a real-time monitoring application for aerospace manufacturing data, with live telemetry ingestion, historian validation, and dashboards designed for operational use.
- Telemetry ingestion through MQTT
- SQL-based historian validation
- Dashboard design for operational users
Previous career
Water infrastructure field operations
Worked in environments where reliability, maintenance discipline, traceability, and practical incident response were central to daily operations.
- Critical infrastructure operations
- Maintenance discipline and troubleshooting
- Process awareness and accountability
Education
Bachelor's in Applied Computer Science
Transitioned into software and data systems with a clear interest in streaming, data pipelines, and industrial digitalization.
- Applied Computer Science degree completed in 2025
- Data-oriented systems and software foundations
- Bridge from operations into engineering
Skills
Tech stack & tools
Core competencies in Python, SQL, and real-time messaging, with active expansion into distributed streaming systems.
Programming & data
Python, SQL, PostgreSQL, Git, Linux, data modeling basics
Messaging & ingestion
MQTT, JSON payload processing, REST APIs, real-time telemetry flows
Platforms & workflow
Docker, Azure exposure, Agile/Scrum, industrial collaboration contexts
Currently learning
Currently completing the DataTalksClub Data Engineering Zoomcamp with focus on Kafka, Spark Streaming, and distributed systems.
About
From field operations to data engineering.
Nearly a decade in water infrastructure came before the move into software and data systems. That background shaped a practical way of thinking about uptime, failure modes, and reliable monitoring.
What shapes my approach
Systems should stay reliable under real-world pressure.
An operational background shapes the approach to data engineering: ingestion should be dependable, monitoring should be actionable, and pipelines should support real decisions.
Why me
What I bring that most junior data engineers don't
Experience-led proof points focused on uptime, graceful degradation, industrial constraints, and real streaming systems.
01
Operational intuition from 9 years in the field
This background is not only about moving data, but understanding the systems that generate it. Years in critical infrastructure built strong instincts around uptime, failure modes, and what reliability means at 3 AM.
At Sonaca: designed MQTT reconnection logic and last-known-value persistence because connections fail and systems need graceful degradation.
02
Real production experience with streaming data
Production work included a system processing live MQTT telemetry at 500ms latency in an aerospace manufacturing environment with IEC-62443 constraints and real operational users.
Not a tutorial. Production system used by maintenance supervisors daily.
03
Industrial IoT domain expertise that's rare in juniors
Both sides of the conversation are familiar: sensors, PLCs, SCADA, and historian data on one side; pipelines, APIs, and data models on the other.
Collaborated with IT, maintenance, IoT, and cybersecurity teams and translated across all of them.
What this means for you
You get a junior who already understands industrial reality, streaming constraints, and why resilience and monitoring matter.
Roadmap
12-month development plan
Measurable milestones focused on streaming systems, public projects, and production readiness.
Open 12-month roadmap and public commitment
Months 1–3
Foundation + first portfolio project
Completing DataTalksClub Zoomcamp modules 1–4. Building foundational skills in Docker, PostgreSQL, Airflow, dbt, and BigQuery.
- Module 1: Docker & Postgres — completed
- Modules 2–4: Orchestration, warehouse, dbt — in progress
- First public project: end-to-end pipeline with orchestration on GitHub
Months 4–6
Streaming specialization — my competitive edge
Deep dive into Kafka and Spark Streaming (Zoomcamp modules 5–7). This is where MQTT production experience at Sonaca translates to enterprise streaming systems.
- Kafka fundamentals: producers, consumers, topics, partitions
- Spark Streaming with Kafka integration
- Second project: real-time IoT pipeline — Kafka + Spark + dashboard
- dbt Analytics Engineer certification
Months 7–9
Active job search + portfolio polish
Three polished GitHub projects. Active applications to remote-first companies in IoT, utilities, fintech, and smart infrastructure. SQL interview prep and system design basics.
Target: first remote junior data engineering role with focus on streaming or industrial IoT.
Months 10–12
First role + freelance foundation
Employed remotely, contributing to production pipelines while learning from senior engineers. Building reputation through LinkedIn articles about industrial IoT and data. First small freelance contract on the side.
End state: confident streaming data engineer with production Kafka/Spark experience, ready for year 2–5 growth toward freelance independence.
Public commitment
Progress tracked in public
This roadmap is being tracked publicly on GitHub and LinkedIn. Every completed milestone gets documented. It is not a vague learning plan, but a structured path with accountability.
Track my progressLanguages
Romanian, English, French
Romanian native speaker, fluent English, and French at C1 level.
Contact
Contact
Open to junior data engineering roles in streaming, IoT, and industrial data systems.
Email
georgescumvlad@gmail.com
LinkedIn
linkedin.com/
GitHub
github.com/vladgme
Location
Belgium · Remote or hybrid within Europe
Open to
Junior Data Engineer · Streaming · IoT · Industrial Data