Altaf Fahdi - Data Scientist

Overview

Data engineer focused on building and scaling governed cloud lakehouse data platforms for healthcare and financial/commerce domains. Experienced in designing ETL/ELT and real-time pipelines using Python, SQL, PySpark, Spark, Airflow, dbt, and warehouse/lakehouse stacks including Snowflake and Databricks/Delta Lake. Provides engineering leadership through architecture reviews, delivery coordination, and incident resolution for production data systems.

Technical skills

Java

Python• Senior • 11y+

SQL• Senior • 11y+

Scala• Junior • 6y+

Python

pySpark• 6y+

Databases

PostgreSQL

Amazon Redshift• 9y+

Apache Kafka• 9y+

Snowflake• 9y+

Apache Iceberg• 6y+

Databricks• 6y+

Delta Lake• 6y+

Google BigQuery• 6y+

AI/ML

Anomaly Detection

Hadoop

NumPy

Flink• 9y+

Spark• 9y+

Airflow• 6y+

dbt• 6y+

DevOps

AWS

AWS Lambda

Azure

CI/CD

GCP

Incident Management

Netlify

Rest API

SLI/SLO/SLA

Splunk

Docker• 6y+

Git• 6y+

Kubernetes• 6y+

Terraform• 6y+

Cybersecurity

HIPAA

PCI DSS

Cryptography

Vault

Timeline

Lead Data Engineer • Lead

DeepScribe • Full-Time

Apr 2023 to Present 3 Years 5 Months In office

Architected cloud-based healthcare data platforms for AI-assisted medical coding, clinical analytics, and regulatory reporting. Built reusable Databricks and Spark frameworks for schema enforcement, standardization, exception handling, and model-ready dataset preparation. Orchestrated governed lakehouse pipelines with Airflow and dbt, improving reliability and performance while reducing failed runs and optimizing Snowflake and Spark workload execution. Implemented HIPAA/PHI safeguards including encryption, RBAC, masking, auditing, and secure access controls.

Databricks

Spark

Airflow

dbt

Delta Lake

Snowflake

pySpark

Python

SQL

Senior Data Platform Engineer • Senior

Onehouse • Full-Time

Apr 2020 to Mar 2023 2 Years 11 Months In office

Designed multi-cloud data platforms for SaaS analytics, product intelligence, and ML data consumption across AWS, Azure, and GCP. Built batch and incremental pipelines with Python, SQL, PySpark, Airflow, and dbt, and implemented reusable ingestion frameworks spanning S3/ADLS/GCS, REST APIs, and Kafka. Migrated enterprise workloads to a lakehouse stack using Snowflake, Databricks, Delta Lake, and Apache Iceberg, with modular dbt models and automated data quality checks. Containerized services with Docker, deployed workloads on Kubernetes, and used Terraform with CI/CD to improve scalability and reduce processing costs.

Python

SQL

pySparksince 2020

Scala

Airflowsince 2020

dbtsince 2020

Databrickssince 2020

Snowflake

Delta Lakesince 2020

Apache Iceberg

Apache Kafka

Docker

Kubernetes

Terraform

Google BigQuery

Git

Cloud Data Engineer • Middle

Current • Full-Time

Nov 2017 to Mar 2020 2 Years 4 Months In office

Built AWS data platforms for payments and transaction analytics, including settlement, reconciliation, fraud-risk operations, and financial reporting. Integrated payment APIs, relational sources, and third-party data using Python, SQL, AWS services, and lakehouse/warehouse tooling with Snowflake and Redshift. Implemented near-real-time streaming workflows using Kafka, Spark Structured Streaming, and Flink, supporting suspicious-transaction monitoring and ML fraud dataset preparation. Automated scheduling and recovery with Airflow and used operational monitoring with Splunk and CloudWatch while applying PCI-DSS controls through encryption, IAM restrictions, tokenization, and audit logging.

Python

SQL

Amazon Redshift

Snowflakesince 2017

Sparksince 2017

Apache Kafkasince 2017

Flink

ETL/ELT Engineer • Middle

OroCommerce • Full-Time

Jun 2015 to Oct 2017 2 Years 4 Months In office

Developed SSIS and SQL Server ETL workflows for commerce data including product catalogs, customers, orders, payments, inventory, and marketplace activity. Integrated application databases and external systems using SQL, Python scripts, and file/API-based ingestion patterns. Modeled star-schema datasets and reporting tables for analytics dashboards, defining mappings, transformation rules, validation checks, and supporting stored procedures. Improved pipeline stability through automation, query tuning, reconciliation controls, and controlled troubleshooting and reload procedures.

SQLsince 2015

Pythonsince 2015