Zakariaa Bohsine

Designed and industrialized end-to-end data pipelines on the Microsoft Azure data platform to support analytics and business intelligence use cases. Developed and maintained scalable data transformations using Azure Databricks (PySpark / SQL) for large-scale data processing. Orchestrated data ingestion and workflows with Azure Data Factory, ensuring reliable and automated data movement. Built analytical data models optimized for reporting and downstream consumption in Power BI. Implemented data quality, validation, and consistency checks across datasets (completeness, freshness, anomaly detection). Collaborated with business and analytics teams to translate requirements into robust, production-ready data solutions. Delivered documented, maintainable, and scalable data architectures aligned with enterprise best practices.

Design and operate Python‑ and SQL‑centric, cloud‑native data pipelines that fuel predictive models and self‑serve analytics. Own the full stack, dbt models, Snowflake warehousing, Airflow orchestration, Power BI dashboards, while delivering through GitLab CI/CD, tracking work in Jira, and documenting standards in Confluence. Partner closely with data scientists and business teams to turn raw events into actionable customer insights. Key contributions * Python + SQL transformations: authored and maintained dbt models on Snowflake, embedding rigorous tests and lineage for trusted analytics. * End‑to‑end orchestration: automated ELT workflows in Airflow (Snowflake → dbt → Power BI) with GitLab CI/CD pipelines for linting, unit tests, and scheduled deployments. * Machine‑learning enablement: supplied clean feature tables and automated scoring feeds for a customer‑churn prediction initiative. * Insight delivery: built interactive Power BI reports that translate complex metrics into clear, decision‑ready visuals for marketing, product, and finance. Process & governance: led Agile rituals in Jira and built a Confluence knowledge base for runbooks, coding standards, and incident playbooks. Core skills & tools Python · SQL · dbt · Snowflake · Apache Airflow · GitLab CI/CD · Power BI · Docker · Git · Data Modeling · ELT / ETL · DataOps · Jira · Confluence · Stakeholder Communication · Agile / Scrum

Summary: Designed and maintained scalable data pipelines to automate data ingestion, transformation, and delivery across multiple environments using Apache Airflow, DBT, and Snowflake. Spearheaded initiatives to improve data quality and observability, while enabling faster decision-making through well-structured datasets and clean data models. Key Contributions: -Built robust ELT pipelines with Airflow to orchestrate workflows and monitor data freshness. -Modeled and transformed raw datasets using DBT, applying modular and testable logic in a production environment. -Managed data warehousing on Snowflake, optimizing query performance and cost efficiency. -Collaborated closely with data analysts and stakeholders to ensure pipelines aligned with business KPIs. -Integrated ML-driven features into pipelines for predictive insights and automated reporting. -Delivered clear, actionable dashboards to senior management, using cleaned and modeled data. -Worked with AWS services (e.g., S3, Lambda, EC2) to deploy and manage infrastructure.

Worked on telecom data analysis, delivering insights for network optimization; built ETL pipelines to streamline data collection and preparation.

Conducted data analysis to identify key trends and insights in customer service quality; developed interactive dashboards and detailed reports to support data-driven decision-making; collaborated with stakeholders to define metrics and improve business processes based on data analysis; ensured data quality and consistency across multiple sources.