Talel Taieb
A
A
Data Analyst & Engineer – I build automated data pipelines and business dashboards.
From Airbus to freelance projects, I turn raw data into smart, scalable systems that improve visibility, decision-making, and performance.
Specialized in dbt, Airflow, Streamlit, and Python – let’s automate what matters.
About
I’m a hybrid Data Analyst & Engineer with 3+ years of experience turning raw data into clear, automated systems for decision-making. My work blends data pipeline automation, dashboard design, and business-focused analytics to help teams move faster and act smarter.
At Airbus, I led end-to-end data initiatives across HR, design, and manufacturing — including:
- Automating KPI pipelines using dbt + SQL
- Building alerting systems with anomaly detection models
- Deploying dashboards in Streamlit, Power BI, and Tableau
I specialize in making data tools that are clean, scalable, and actually used — not just theoretical models. I care about results: faster insights, clearer visibility, and fewer bottlenecks for product and ops teams.
- Languages: Python, SQL, Bash
- Automation & Data Engineering: dbt, Airflow, Git, Skywise, APIs, Docker
- Visualization: Streamlit, Power BI, Tableau, Looker Studio
- Machine Learning: scikit-learn, NLP (BERT, transformers), anomaly detection
- Cloud & Platforms: GCP (BigQuery), AWS (basic), Redshift, Snowflake
I'm currently focused on remote and freelance missions — helping teams build automated reporting systems, ETL workflows, and real-time dashboards. Let’s simplify your data and turn it into action.
Because great data work isn't about complexity — it's about clarity, speed, and usability.
Experience
- Built an end-to-end KPI automation pipeline using dbt, SQL, and Skywise to feed interactive dashboards for HR and product teams.
- Deployed real-time dashboards in Streamlit, Tableau, and Power BI — used by 5 cross-functional teams to track operations and priorities.
- Developed alerting systems using anomaly detection models (Isolation Forest + rule-based logic) to flag high-risk engineering changes.
- Extracted insights from technical documents with BERT-based NLP models, improving auditability and decision speed.
- Collaborated with engineers, HR leaders, and PMs to define KPIs, improve data pipelines, and ensure delivery of actionable tools.
- Tools: Python, SQL, dbt, Airflow, Streamlit, Tableau, Power BI, Skywise, scikit-learn, Transformers
Projects
A full-stack data pipeline that surfaces key retail insights using Airflow, dbt, and Streamlit.
Tools: Airflow, dbt, Snowflake, Streamlit, SQL
Problem: Retail managers manually track weekly sales KPIs across 50+ stores—time-consuming and error-prone.
Solution: Built an Airflow DAG to ingest raw sales data (S3 → Snowflake), transform with dbt, and refresh a Streamlit dashboard every morning.
Tech & Orchestration: Airflow (daily schedule + retry logic) → Snowflake warehouse → dbt models & tests → Streamlit UI
Impact: Reduced manual KPI collection from 4 hours/week to 5 minutes; enabled real-time trend spotting and drill-downs.
A data dashboard for project managers, built with real-world e-commerce data.
Tools: Python, Pandas, Plotly, Streamlit, Streamlit Cloud, Kaggle
Problem: Online retailers juggle dozens of metrics—conversion, cart abandonment, revenue per channel—using disconnected spreadsheets.
Solution: Built an Airflow DAG to ingest raw sales & customer data into Redshift, transform with dbt, then publish an interactive Streamlit dashboard that unifies metrics in one place.
Tech & Orchestration: Airflow (daily schedules + failure alerts) → Amazon Redshift → dbt models & tests → Streamlit UI
Impact: Cut manual reporting from 5 hours/week to under 10 minutes; empowered marketing & product teams with on-demand insights.
A full-stack ML app that predicts telecom customer churn with live inputs and risk insights.
Tools: Python, Streamlit, scikit-learn, XGBoost, SHAP, SMOTE
Problem: Telecom operators struggle to spot at-risk customers before they leave, leading to reactive retention efforts.
Solution: Orchestrated a pipeline that cleans Telco data (Kaggle), trains a churn model daily (XGBoost + SHAP), writes scores to Snowflake, and updates a Streamlit alert dashboard.
Tech & Orchestration: Airflow (monthly retrain + daily scoring) → Snowflake warehouse → Python ML (XGBoost, SHAP) → Streamlit dashboard with email/slack alerts
Impact: Identified top 5% high-risk accounts each morning, enabling targeted retention and reducing churn by an estimated 15% in simulation.
A multilingual NLP app that classifies customer reviews as positive or negative using BERT.
Tools: HuggingFace Transformers, BERT, Deep Translator, Streamlit
Problem: Global brands need real-time sentiment across multiple languages but often rely on manual translation and siloed tools.
Solution: Built an Airflow DAG to ingest tweets, translate text with Hugging Face pipelines, run sentiment classification, and store results in BigQuery. A Streamlit dashboard visualizes sentiment trends by language and region.
Tech & Orchestration: Airflow (hourly fetch + translate jobs) → Google BigQuery → Python (Hugging Face, fastAPI) → Streamlit UI
Impact: Automated sentiment reports across 5 languages, cut manual translation/analysis time by 90%, and provided marketing teams with instant brand health insights.
Skills
Languages & Databases
Python
SQL
PostgreSQL
Bash
Snowflake
PySpark
Redshift
Python Libraries
Pandas
NumPy
Matplotlib
Seaborn
scikit-learn
DuckDB
Hugging Face
Frameworks & Dashboards
Streamlit
FastAPI
Tableau
Looker Studio
Power BI
Looker
Machine Learning & AI
TensorFlow
PyTorch
Spark MLlib
MLflow
Cloud & Tools
Git
Docker
AWS
Airflow
dbt
Testimonials
Education
Toulouse, France
Degrees: Double Master’s Degrees in Applied Mathematics & Computer Science
Specialization: Hybrid Artificial Intelligence (ML + Deterministic & Probabilistic Modeling)
Type: Apprenticeship-based Engineering Program (Airbus)
- Machine Learning & Deep Learning
- Probabilistic Models & Inference
- Optimization & Decision Systems
- Big Data Architecture
- Advanced Python & Data Engineering
Relevant Courseworks:
French Scientific Baccalauréat
Tunis, Tunisia
Specialization: Mathematics & Physics
Status: Independent Candidate (Candidat libre)
- Advanced Mathematics
- Physics & Chemistry
- Earth and Life Sciences (SVT)
- Philosophy
- French Language and Literature
- English as a Foreign Language
Core Subjects: