Shahul Mohamed

Context

Complete refactoring of the data asset of a major real estate player, with the objective of centralizing heterogeneous flows within a secure, scalable, and cost-optimized Snowflake Cloud Data Platform, for BI use cases.

Progressive migration from legacy systems (Business Objects, Oracle, SQL Server), with significant challenges in quality, performance, and non-regression.

Team

Close collaboration with Data, BI, and business teams, in a hybrid, production-oriented environment.

My Role

As a Senior Data Engineer, I am involved in the entire platform lifecycle:

- Design of the multi-environment Snowflake architecture (DEV / UAT / PROD)

- Implementation of data governance (RBAC, principle of least privilege)

- Definition and implementation of a Medallion architecture (Bronze / Silver / Gold)

- Development of modern ELT pipelines with dbt (macros, tests, incremental)

- Advanced optimization of Snowflake performance (Cluster Keys, Query Profile, Search Optimization)

- Cost management and FinOps practices (Resource Monitors, auto-suspend, warehouse sizing)

- Implementation of security and GDPR compliance mechanisms (Dynamic Masking, Row Access Policies)

Tools / Environments

Snowflake, advanced SQL, Python (Snowpark), dbt, Airflow / Snowflake Tasks, AWS S3.

Results

- Design and deployment of a Snowflake Data Platform from scratch, ready for large-scale BI use cases

- Average reduction of 25% in Snowflake costs through FinOps optimizations

- 30% improvement in response times for critical queries

- Secure, governed, and scalable platform, aligned with modern data standards

Context

Modernization of the decision-making system managing critical data for over 100 million insured individuals, within a strict regulatory framework involving sovereignty, performance, and reliability concerns.

Objective: improve processing times and robustness of high-volume data pipelines.

Team

Collaboration with Data Engineering and architecture teams, in a demanding production-oriented environment.

My Role

As a Senior Data Engineer, I tackle issues related to massive volumes and process optimization:

- Implementation of a secure data virtualization architecture via Starburst / Trino

- Management of massive data migration (CSV → Parquet)

- Advanced optimization of PySpark jobs on Databricks (memory tuning, parallelism, partitioning)

- Contribution to the target architecture and Data Engineering best practices

- Improvement of the reliability and performance of critical regulatory processes

Tools / Environments

Databricks, PySpark, Starburst (Trino), PostgreSQL, Python.

Results

- Migration of over 500 TB of data to optimized formats

- 45% reduction in nightly processing times

- Halving of execution time for critical regulatory processes

- More robust data pipelines suitable for large-scale production constraints

Context

Cloud transformation of an international software publisher through the implementation of an industrialized GCP Landing Zone to accelerate Data projects.

My Role

- Design of target Cloud architectures (HLD / LLD)

- Complete industrialization via Terraform (IaC)

- Implementation of CI/CD pipelines (Cloud Build, dbt)

- Environment security (IAM Least Privilege, KMS, compliance)

- Cost and resilience optimization (HA / DR, SRE, autoscaling)

Results

- Reproducible, secure, and scalable Cloud environments

- Reduced time-to-market for data projects

- Better control of Cloud costs through FinOps / GreenOps practices