Open to full-time Data Engineer roles & freelance missions

I build data pipelines teams can trust.

I’m Imad, a Data Engineer in Paris. I design batch & streaming pipelines with Airflow, Spark, Kafka and dbt — from raw sources to the dashboard — with data quality built in, not bolted on.

Portrait of Imad Bouzkraoui
SELECT role FROM me;Data Engineer
DAGimad_data_platform@hourly
extract_apiREST · SaaS APIsextract_dbPostgres · Mongoscrape_webPlaywrightingest_rawKafka → S3transformSpark · dbtdq_checksrules + MLload_goldSnowflakeserve_biPower BIserve_mlfeatures · API
run_id: scheduled__…queued
  • 5SaaS apps consolidated into one Airflow pipeline on AWS
  • h → minbug detection time with a real-time Power BI × Sentry dashboard
  • ~30 fpslive video processed in real time for incident detection
  • 100%Job Success Score as a freelancer on Upwork

01About

Reliable data is a product, not a by-product.

I’m a Data Engineer with a State Engineering degree in Data Engineering from ENSA Al Hoceima. I’m now completing a specialised Master’s at Institut F2I in Paris, in a work-study role at VirtuoSys, a LegalTech SaaS company.

I care about the unglamorous part of data: pipelines that are idempotent, monitored and documented, so the people downstream — analysts, product teams, business owners — can stop wondering whether the numbers are right. I’ve done that for a bank, a SaaS company and a computer-vision team.

Alongside that, I freelance on Upwork — scrapers, automation and data pipelines — with a 100% Job Success score. Off the keyboard: running, strength training, learning the saxophone, and community work as former President of the SADIM association and Vice-President of the ENSAH Data Club.

Based in
Paris, France · From Morocco
Education
Specialised Master’s in Data EngineeringInstitut F2I / DSP · Paris · 2025 — presentState Engineering Degree, Data EngineeringENSA Al Hoceima · 2020 — 2025
Languages
French (fluent) · English (fluent) · Arabic (native)
Certifications
IBM · Python for Data Science (2024)IBM · Data Analysis with Python (2023)IBM · SQL and Relational Databases (2023)
whoami.sqlPostgreSQL 16
SELECT name, role, based_in, focus
FROM   engineers
WHERE  pipelines = 'reliable'
  AND  data_quality IS NOT NULL
ORDER BY impact DESC
LIMIT  1;
namerolebased_infocus
Imad BouzkraouiData EngineerParisbatch · stream · DQ

✓ 1 row returned · 0.042 s

02Experience

Where I’ve built data systems

  1. Dec 2025 — Present

    Data Engineer · Work-study· VirtuoSys

    SaaS LegalTech · Paris

    • Built Airflow pipelines on AWS (S3, EC2) consolidating logs, customer data and usage metrics from 5 heterogeneous SaaS applications.
    • Integrated Power BI with the Sentry API: a real-time self-service dashboard (3 pages, 5 projects, ~12 KPIs, GDPR-compliant) that replaced manual bug reporting — detection went from hours to minutes.
    • Scraped and ingested public LegalTech contacts into an internal CRM I designed; contributed to a chatbot integration pipeline.
    • Industrialised deployments with GitLab CI/CD; built an AI-assisted SEO content pipeline; owned data documentation and governance.
    AirflowAWS S3AWS EC2PythonSQLPower BISentry APIGitLab CI/CDDocker
    Case study
  2. Feb — Aug 2025

    Data Engineer · Data Quality (final-year internship)· Banque Centrale Populaire

    Data Governance · Casablanca

    • Designed “Data Watchdog”: moved quality controls from hard-coded rules to ML-based anomaly classification (scikit-learn).
    • Orchestrated batch & streaming flows with Prefect and Kafka, with real-time alerts and a human validation loop where business teams accept or reject corrections.
    • Governance & security: access control with Apache Ranger, lineage and cataloguing with Apache Atlas; Power BI quality reporting (completeness, freshness, anomalies).
    PythonSQLPrefectKafkaPostgreSQLscikit-learnApache RangerApache AtlasPower BIDocker
    Case study
  3. Jun — Aug 2024

    Data Engineer · Deep Learning (internship)· Nextronic

    Smart video surveillance

    • Built a real-time pipeline on live camera feeds (~30 fps) classifying 5 incident types (fire, intrusion…), alerting only on confirmed detections (3 s persistence + scoring).
    • Assembled the training dataset (Kaggle + targeted scraping per incident type) and fine-tuned a pre-trained Hugging Face model.
    PythonOpenCVTensorFlowHugging FaceNumPy
    Case study
  4. 2021 — Present

    Freelance Data & Automation Engineer· Upwork

    Remote · international clients

    • Web scraping, workflow automation and data pipelines for clients — with a 100% Job Success score.
    • Social-platform data extraction (Instagram, Reddit, LinkedIn), bots that run unattended, and clean datasets delivered ready for analysis.
    PythonSeleniumPlaywrightBeautifulSouppandas

03Selected work

Case studies

Production pipelines and the projects behind them — what the problem was, what I built, and what changed.

Production · SaaS LegalTech

One view of data and bugs across 5 SaaS products

Airflow pipelines on AWS and a real-time Power BI × Sentry dashboard that took bug detection from hours to minutes.

5
SaaS apps consolidated
~12
KPIs in real time
h → min
bug detection time
AirflowAWS S3AWS EC2Python+5
Read case study

Production · Banking

Data Watchdog: data quality that learns

From hard-coded rules to ML anomaly classification — with streaming alerts and a human validation loop — for a bank’s Data Governance team.

Rules + ML
hybrid anomaly detection
Batch + stream
Prefect & Kafka
Human-in-the-loop
business validation
PythonSQLPrefectKafka+6
Read case study

Project · Analytics engineering

Airbnb analytics, the Modern Data Stack way

End-to-end analytics engineering: S3 → Snowflake → dbt, Medallion layers, SCD Type 2 history and tested models.

3
Medallion layers
SCD2
history via dbt snapshots
4
materialisation types
dbt CoreSnowflakeAWS S3AWS IAM+3
Read case study

Production · Computer vision

Real-time incident detection on live video

~30 fps camera streams classified into 5 incident types, with confirmation logic so a single ambiguous frame never raises an alarm.

~30 fps
live streams
5
incident types
3 s
confirmation window
PythonOpenCVTensorFlowHugging Face+1
Read case study

More projects

  • DataOps

    CI/CD for data pipelines

    Every git push is tested, containerised and deployed as an Airflow DAG by Jenkins — then the run is verified in MongoDB.

    Jenkins · Docker · Airflow · MongoDB · pytest

  • Batch processing

    Customer 360 with PySpark

    Cleans, joins and consolidates a relational database, MongoDB and JSON delivery events into one customer_order_360 view — with a quality audit and reject handling.

    PySpark · Spark SQL · PostgreSQL · MongoDB

  • Open data

    RankRace — data-viz video pipeline

    A reproducible pipeline that turns World Bank and Our World in Data series into animated bar-chart-race videos, from download to final render.

    Python · pandas · Matplotlib · ffmpeg

  • Web data

    Instagram profile scraper

    Extracts and structures multimedia content from Instagram profiles on dynamic, JavaScript-heavy pages.

    Python · Selenium · BeautifulSoup

  • Web data

    Reddit data collection

    Scripts that collect posts and discussions from Reddit into analysis-ready datasets.

    Python · Jupyter · pandas

  • Everything else is on GitHubgithub.com/ImadBK01

04Stack

The toolbox

Tools I’ve shipped to production or built complete projects with — no logo collection.

01Orchestration & processing

  • Apache Airflow
  • Prefect
  • Apache Kafka
  • Apache Spark (PySpark)
  • dbt
  • Batch & streaming

02Languages

  • Python
  • SQL
  • PL/SQL
  • pandas
  • NumPy
  • Jinja

03Storage & architecture

  • Snowflake
  • PostgreSQL
  • MongoDB
  • MySQL
  • S3 data lake
  • Lakehouse
  • Medallion

04Cloud & DevOps

  • AWS (S3, EC2, IAM)
  • Docker
  • GitLab CI/CD
  • Jenkins
  • Git
  • Linux

05Quality & governance

  • Data quality
  • Monitoring & alerting
  • Apache Atlas
  • Apache Ranger
  • Sentry
  • GDPR

06BI, ML & scraping

  • Power BI (DAX)
  • scikit-learn
  • Hugging Face
  • OpenCV
  • LLM / RAG
  • Selenium
  • Playwright

05Freelance

Need a data engineer for a mission?

Alongside my data engineering role I take on a small number of freelance missions — through Upwork or a direct contract. Same standards as in production: tested, documented, monitored.

  • Data pipelines & orchestration

    Batch or streaming pipelines from APIs, databases and files into a clean, queryable store — scheduled, retried and monitored.

    Airflow · Prefect · Kafka · Spark

  • Warehouse & Modern Data Stack

    A warehouse your analysts can trust: Medallion layers, tested dbt models, history of changes and documentation.

    dbt · Snowflake · PostgreSQL · AWS

  • Web data extraction & automation

    Robust scrapers and bots that run unattended and deliver clean, deduplicated datasets — or remove a manual task for good.

    Python · Selenium · Playwright

  • Dashboards & data quality

    Power BI dashboards on top of monitored data, with checks and alerts that catch bad data before it reaches a decision.

    Power BI · Sentry · dbt tests

How a mission runs

  1. 01Discovery call30 minutes, free: your data, your goal, your constraints.
  2. 02Scoped proposalClear deliverables, timeline and price — fixed or hourly.
  3. 03Build in the openRepository access from day one and regular demos.
  4. 04HandoverDocumentation, a runbook and a walkthrough so your team owns it.

07Contact

Let’s build something reliable.

Hiring a data engineer, or need help with a pipeline, a warehouse or a scraping project? Send me an email or a WhatsApp message — I reply within 48 hours.

Open to full-time Data Engineer roles & freelance missions

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