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Azan Butt

@azanbutt

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Staff Data Engineer driving scalable cloud-native data platforms and analytics.

United States

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What I'm looking for

I seek senior engineering roles building scalable, secure cloud data platforms with strong collaboration, ML integration, observability, and opportunities to mentor and standardize best practices.

I am a Staff Data Engineer with 10+ years of experience architecting and scaling cloud-native data platforms on Azure, Databricks, and Spark, delivering high-volume ETL/ELT pipelines and multi-layered data architectures that power analytics and AI.

I have implemented data governance, lineage, PII/PHI protection, SOC2 and NIST-aligned controls, CI/CD, observability, and performance optimizations for distributed systems while integrating SaaS sources and machine learning workflows into production.

I lead and mentor teams, standardize engineering best practices, and collaborate cross-functionally to deliver durable, secure, and high-performance data solutions that support executive decision-making and business outcomes.

Experience

Work history, roles, and key accomplishments

Current

Staff Data Engineer

Current

DataGenius Technologies

Apr 2022 - Present (3 years 11 months)

Architected enterprise-scale, cloud-native data platforms on Azure, Databricks, and Spark; built multi-layer ETL/ELT pipelines and semantic models that powered analytics and ML workflows while enforcing governance and SOC2/NIST-aligned security.

Azure Databricks Apache Spark ETL Data Governance Data Modeling CI CD Observability Spark Tuning

Senior Data Engineer

Addepto

Jun 2016 - Mar 2022 (5 years 9 months)

Designed and implemented scalable production data pipelines using Python, SparkSQL, and Airflow; canonicalized raw data into governed models and integrated ML feature pipelines to improve performance and reliability.

Python Apache Spark Airflow AWS SparkSQL Data Modeling Streaming Kafka Data Quality

Data Engineer

Saviant Consulting

Apr 2013 - Jun 2016 (3 years 2 months)

Built and maintained scalable ETL/ELT pipelines with Python, PySpark, and dbt on AWS/Databricks/Snowflake; implemented orchestration, validation, and observability frameworks for reliable production data processing.