Data Engineer & MLOps · Brussels
Building reliable batch and streaming data pipelines.
I am a Data Engineer based in Brussels, with a diverse engineering background spanning over a decade. I am currently finalizing my MSc in Applied Computer Science while completing an intensive Data Engineering Masterclass focused on AWS, Big Data, and Modern Data Architectures.
My background in maintaining critical medical systems taught me the value of reliability, compliance (FDA/GDPR), and zero-downtime operations. Today, I apply that same discipline to build robust data pipelines. I specialize in Real-time Streaming (Kafka/Flink), Cloud Infrastructure, and Data Security.
Technologies I use to build reliable data pipelines.
Python (Pandas, PySpark, PyFlink), SQL (PostgreSQL, ClickHouse),Java, Bash.
Apache Airflow, Docker, Kubernetes.
Apache Kafka, Apache Flink, Spark Streaming, Debezium (CDC).
AWS (Glue, Athena, Lambda), GCP (BigQuery).
Grafana (dashboards/alerts), Tableau, Power BI.
Databricks (Delta Lake, DLT), Snowflake, dbt, Apache Spark.
Systems I design, build, and monitor — from raw source data to production-ready pipelines.
Building reliable Batch and Streaming pipelines to move data from sources to storage using Kafka & Flink.
Designing clean data models to ensure data is organized, fast, and ready for analytical queries.
Setting up Monitoring (Grafana) to catch issues early and ensure pipelines are running smoothly 24/7.
Implementing automated tests to ensure data is accurate, consistent, and trustworthy.
Creating clear Dashboards (Tableau) to help stakeholders visualize trends and make decisions.
Combining engineering discipline with modern data skills.
Intensive, project-based Data Engineering & MLOps bootcamp. Working hands-on with PostgreSQL, ClickHouse, Apache Kafka, Apache Spark, Apache Flink, Delta Lake, Docker, Kubernetes, Apache Airflow, MLflow, Jenkins and AWS.
Capstone projects include:
– Real-time fraud detection pipeline
(Kafka, Spark, PostgreSQL, MySQL, Redis, Grafana, AWS)
– End-to-end ML model deployment with monitoring and security on the cloud
– Building a secure Rust-based message broker (CipherMQ) as a product-focused project
Worked with Google Cloud Platform (GCP) and Power BI to visualize complex datasets. Supported ETL processes and created clear technical reports to help business stakeholders make decisions.
Maintained complex medical imaging systems with 99% uptime. Performed root-cause analysis on system logs and ensured strict compliance with FDA and local healthcare regulations.
Operated high-tech radiology equipment in a fast-paced clinical environment, delivering accurate results while following strict safety protocols.
Current and selected academic work relevant to my transition into data engineering.
Graduate program focused on Data Engineering, Machine Learning, Big Data, and Distributed Systems. Relevant coursework includes Big Data Processing, AI Techniques, Advanced Databases, Cloud & Distributed Systems, and Advanced IT Networks.
Projects that best represent my work in streaming systems, analytics engineering, privacy, and MLOps.
Production-style CDC pipeline for a food-delivery platform: PostgreSQL changes flow into Kafka, Flink SQL materializes streaming views, and online outputs land in OpenSearch and Redis. Built with Docker Compose, repeatable connector registration, and verification scripts.
Privacy-by-design demo built fully inside PostgreSQL. Combines dynamic masking, trigger-based anonymized streaming into a sanitized schema, and static anonymization for safe data exports in GDPR-conscious environments.
Analytics engineering workflow using Snowflake sample data, dbt models and tests, and Airflow via Astronomer Cosmos. Produces a clean fact table with automated data-quality checks and orchestration around transformation dependencies.
FastAPI-based fraud scoring service with health checks, feedback capture, monitoring endpoints, Prometheus, Grafana, and MLflow integration. Designed as a practical starting point for near real-time model operations.
Containerized fraud-detection pipeline where a producer generates synthetic transactions, Spark Structured Streaming computes features in real time, PostgreSQL stores scores, and Airflow orchestrates drift detection and retraining tasks.
Selected courses and certificates that support my data engineering path.
Data Engineering School · Issued Mar 2025
CI/CD, PostgreSQL, Git & GitHub, Kubernetes, Apache Airflow, Docker, AWS, Apache Kafka, ETL.
Snowflake · Issued Jun 2025
Data warehousing fundamentals and best practices on the Snowflake platform.
DataCamp · Issued Jun 2025
Analytics engineering with dbt for modular SQL transformations and testing.
Databricks · Issued Apr 2025
Building reliable ingestion pipelines with Delta Lake and the Lakehouse architecture.
Databricks · Issued Apr 2025
Data governance, access control and quality on top of the Databricks Lakehouse.
Maktabkhooneh · Issued Mar 2025
Core cloud concepts, security and cost-awareness for modern cloud platforms.
Coursera · Issued Jun 2021
Classical ML algorithms, logistic regression, neural networks and model evaluation.
Maktabkhooneh · Issued Apr 2021
Advanced Python for data processing, scripting and backend development.
Prefer email? saeidshahriari1@gmail.com