About Firas
Data Engineer with 4 years of experience designing and maintaining scalable data systems in Big Data environments. Strong expertise in SQL for analytical workloads, data modeling, performance optimization, and building robust ETL/ELT pipelines. Experienced in developing distributed data processing solutions using Python and Apache Spark, and orchestrating workflows with Apache Airflow to ensure reliability, scalability, and maintainability.
Hands-on experience working with modern data platforms including Databricks and Snowflake, implementing dimensional models (star/snowflake schemas), optimizing warehouse performance, and transforming raw data into analytics-ready datasets. Comfortable handling large datasets and building production-grade data pipelines aligned with business and analytical requirements.
Cloud-oriented engineer with solid practical experience in AWS and Microsoft Azure services (storage, compute, data integration, and analytics components). Strong foundational understanding of cloud architecture principles, security, and cost optimization, with continuous learning focused on deepening expertise in distributed systems and advanced cloud-native data engineering patterns.
On the BI layer, advanced proficiency in Power BI, including semantic model design, DAX optimization, incremental refresh strategies, row-level security, and performance tuning. Experienced in bridging engineered data platforms with executive dashboards and KPI-driven reporting frameworks.
English
Native or bilingual
French
Fluent
Remote only
Primarily works remotely
Experience
- Santander BankData EngineerBANKING AND INSURANCESeptember 2024 - Today (1 year and 9 months)Warsaw, MZ, Poland• Developed and maintained scalable ETL pipelines using Apache Spark (Java), supporting migration activities and performance optimization in Scala-based environments.• Orchestrated end-to-end data workflows and DAGs using Apache Airflow, ensuring reliable scheduling, monitoring, and automation of data integration processes.• Designed, created, and scheduled Control-M jobs, including Jobs-as-Code implementations, improving deployment consistency, traceability, and maintainability.• Built and optimized data solutions on Databricks, reducing execution times and improving overall pipeline performance and resource efficiency.• Applied rigorous testing practices, including test preparation, execution, validation, and ALM evidence completion, ensuring compliance with quality and audit requirements.• Collaborated in agile delivery teams, leveraging Jira and Confluence for sprint planning, documentation, issue tracking, and cross-team coordination.
- Swarmio MediaData EngineerVIDEO GAMES AND ANIMATIONMarch 2023 - August 2024 (1 year and 5 months)Toronto, ON, Canada• Developed and automated batch ETL pipelines in Databricks using PySpark and Spark SQL to integrate data from Google BigQuery and Amazon S3 into a unified data warehouse reducing data preparation time by 30%.• Built ingestion pipelines with Databricks Autoloader to handle incremental data loads from Amazon S3, improving data freshness and reliability by 35%.• Defined and maintained data warehouse models in Amazon Redshift, optimizing schema design and SQL stored procedures to improve query performance by 25% and ensure scalable data patterns across analytics layers.• Optimized Amazon Redshift queries and table design (distribution/sort keys, compression) to reduce query execution time by 35% and improve dashboard refresh performance across large datasets.• Monitored Databricks jobs on a daily basis, detecting and logging failures or performance bottlenecks, which improved data pipeline reliability by 25% and reduced incident resolution time by 40%.
- Swarmio MediaData AnalystBANKING AND INSURANCEJanuary 2022 - February 2023 (1 year and 1 month)Toronto, Canada• Acted as a bridge between Business, IT, and Data teams, translating operational and financial requirements into functional reporting solutions and validated data models within Power BI and SQL environments.• Defined and implemented business logic and data transformation rules for large-scale datasets (2M+ records), ensuring alignment with financial and operational reporting standards.• Led end-to-end analysis topics from requirement gathering and investigation through solution validation and stakeholder sign-off, ensuring clarity of scope and business acceptance.• Used advanced SQL to analyze datasets, validate data quality, and reconcile reporting outputs, reducing refresh failures by 40% and ensuring 99% reporting reliability.• Automated and standardized P& L, Cash Flow, and Balance Sheet reporting, improving financial transparency and reducing preparation time by 30% while maintaining full audit compliance.• Produced structured documentation of reporting logic, calculation rules (DAX/SQL), and validation processes to ensure knowledge transfer and traceability across teams.
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Education
- M.S. inESPRIT: Engineering school2022M.S. in
- B.S. inESPRIT: Engineering school2020B.S. in
Certifications
- Power BI Data Analyst AssociateMicrosoft2024