Open to opportunities

Alex Salman

@alexsalman

Lead Data Platform Engineer specializing in scalable lakehouse and streaming data architectures for AI/ML-ready analytics.

United States

Message

What I'm looking for

I’m looking to lead architecture and development of scalable data platforms—batch and streaming lakehouse systems on AWS or multi-cloud—supporting analytics and AI/ML. I want teams where I can drive performance, cost optimization, and mentoring through strong engineering practices.

I’m a Senior Data Engineer, Data Architect, and Big Data Engineer with 9+ years designing and implementing scalable data platforms for high-volume enterprise workloads. I translate complex business and technical requirements into production-ready data engineering solutions, spanning batch and real-time processing.

I build cloud-based data ecosystems across AWS, Microsoft Azure, and Google Cloud, leveraging Python, SQL, PySpark, Scala, and Apache Spark for large-scale ETL/ELT, streaming, and lakehouse architectures. I lead architecture and development efforts—using Kafka/Kinesis, Airflow, Databricks, Snowflake, Terraform, CI/CD, and infrastructure-as-code—to improve reliability, performance, and cloud cost management while mentoring engineering teams.

Experience

Work history, roles, and key accomplishments

Current

Lead Data Platform Engineer

Current

DAS42

Jun 2024 - Present (2 years 1 month)

Led the architecture and development of cloud-based data platforms for analytical, operational, and AI/ML workloads across AWS and multi-cloud environments. Designed batch and streaming pipelines and lakehouse architectures, and provided technical leadership across reviews, mentoring, and solution design.

Python SQL PySpark Apache Spark Amazon Kinesis Delta Lake Apache Iceberg Databricks Snowflake Terraform CI CD Git Infrastructure as Code Lakehouse Architecture Medallion Architecture Data Modeling Partitioning Caching

Senior Data Engineer

Relevant Healthcare

Nov 2020 - May 2024 (3 years 6 months)

Engineered scalable ETL/ELT pipelines using Python, SQL, PySpark, and Apache Spark for high-volume enterprise data. Built event-driven near-real-time pipelines, maintained Airflow orchestration, and developed analytics data models using Snowflake, Databricks, Redshift, and dbt.