Omar Hhiri

I am a Data Engineer with 6+ years of experience across data engineering, analytics, and SEO-driven data insights, currently working remotely in the United States. My core strength lies in designing, building, and operating production-grade batch and streaming data pipelines on Azure that support enterprise analytics, reporting, and machine learning workloads.

In my current role at Optum Health (UnitedHealth Group), I own end-to-end data pipelines built with Azure Databricks (PySpark), Azure Data Factory, and Azure Data Lake Storage. I work extensively with Spark to process large-scale structured and semi-structured datasets using a lakehouse architecture (bronze, silver, and gold layers). A significant part of my work involves Spark performance tuning—optimizing partitioning strategies, shuffles, caching, joins, and cluster parallelism—to reduce runtime and cloud costs while maintaining reliability and data quality.

I have hands-on experience building both batch and near-real-time pipelines, including streaming ingestion using Kafka and Azure Event Hubs with Spark Structured Streaming. I focus heavily on pipeline reliability and operational excellence by implementing robust orchestration, retries, monitoring, and SLA enforcement using Azure Data Factory. I also collaborate closely with DevOps teams to integrate data workflows into Git-based CI/CD pipelines via Azure DevOps.

Beyond pipeline development, I place strong emphasis on data governance and analytics readiness. I enforce schema validation, data quality checks, security controls, and compliance requirements, and I work directly with analytics and data science teams to deliver ML-ready datasets and optimize downstream Power BI performance. I regularly provide architectural guidance, conduct code reviews, and mentor junior engineers on Spark optimization, data modeling, and best practices for scalable data systems.

Earlier in my career, I worked as a Data Analyst and SEO Specialist, which gave me a strong business-oriented mindset and a clear understanding of how data pipelines directly impact reporting, digital performance, and decision-making. This background allows me to bridge technical execution with business outcomes effectively.

I have a Master’s degree in Data Analytics and hold Microsoft Azure certifications. I thrive in remote, distributed, Agile environments and am particularly interested in roles that involve large-scale data platforms, cloud-native architectures, streaming systems, and building reliable data foundations for analytics and machine learning.

Interests: scalable data architectures, Spark performance optimization, data governance and lineage, streaming systems, cost-efficient cloud data platforms, and mentoring engineers in production data engineering best practices.