Fati Basit
@fatibasit
Lead Data Engineer and Data Solution Architect building secure, real-time cloud platforms for large-scale analytics.
What I'm looking for
I build and scale enterprise data platforms for healthcare and fintech, supporting “50B+ healthcare records,” millions of daily transactions, and mission-critical analytics. I bring cloud-native architecture and real-time streaming expertise across AWS, Azure, and GCP.
In my current role, I architected and delivered fully cloud-native healthcare pipelines on AWS (S3, Glue, EMR, Redshift), achieving “a 65% reduction in end-to-end processing latency.” I also enforced HIPAA-compliant lake architecture with role-based access controls, full audit logging, and automated data lineage tracking—while migrating from legacy on-prem ETL to cloud ELT with dbt and Apache Airflow.
I lead technical execution and teams: I’ve managed cross-functional delivery of ingestion, transformation, and data quality with engineering standards and code review practices, increasing “delivery velocity by 30%.” I’ve also implemented CI/CD-integrated data quality monitoring with Great Expectations to prevent schema drift and data contract violations from reaching downstream consumers.
Previously, I designed end-to-end real-time fraud detection pipelines using Kafka and Flink, compressing detection from “24 hours to under 5 minutes,” and delivering “$400K+ in annual compute cost savings.” Across roles, I’m most energized by secure, scalable data modernization—turning robust pipelines into business outcomes for analytics, AI, and data-driven decision-making.
Experience
Work history, roles, and key accomplishments
Architected enterprise healthcare data pipelines processing 50B+ clinical records annually on AWS, cutting end-to-end processing latency by 65% and enabling near-real-time analytics for 500+ health plan clients. Migrated legacy ETL to cloud-native ELT with dbt and Airflow, reducing pipeline maintenance by 40% and increasing data model deployment speed by 3x.
Designed real-time fraud detection pipelines with Kafka and Flink, reducing fraudulent claim detection time from 24 hours to under 5 minutes. Optimized Spark jobs on AWS EMR and built Redshift dimensional models, delivering $400K+ annual compute savings and improving data incident rates by 70% quarter-over-quarter.
Built high-throughput ETL pipelines ingesting financial data from 10,000+ banking institutions via REST API and SFTP, enforcing schema validation, idempotency, and SLA compliance. Implemented real-time ingestion on AWS Kinesis processing 2M+ events/day with sub-second latency and automated reconciliation to reduce manual data operations by 60%.
Developed Python and SQL ETL for healthcare data tokenization and de-identification to enable HIPAA-compliant patient record linkage across organizations. Implemented automated validation achieving 99.9% accuracy and improved analytical query performance by 40% through schema design and index/query optimization.
Education
Degrees, certifications, and relevant coursework
Fati hasn't added their education
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Tech stack
Software and tools used professionally
Amazon Redshift
Fivetran
Matillion
Azure Synapse
Apache Spark
Apache Flink
Apache Hive
Talend
Superset
GitHub
GitLab
Kubernetes
Jenkins
GitHub Actions
GitLab CI
dbt
PostgreSQL
Hadoop
HBase
Gmail
.NET
Databricks
Terraform
Azure DevOps
Java
PyTorch
MLflow
Kafka
Apache NiFi
Apache Pulsar
PagerDuty
Grafana
Azure Monitor
Windows
Ansible
Azure Functions
Apache Storm
Airflow
Apache Beam
Time Analytics
Luigi
SQL
Mode Analytics
Dagster
Apache Iceberg
Monte Carlo
Feast
Delta Lake
Great Expectations
Collibra
Harbor
100ms
OpenLineage
Unity Catalog
Factory
Beam
Remote
Safe
Availability
Location
Authorized to work in
Job categories
Skills
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