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Damilola O

@damilolao

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Experienced Data Engineer specializing in scalable data solutions.

United States

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What I'm looking for

I am looking for a challenging role in a collaborative environment where I can leverage my data engineering skills to drive impactful projects and contribute to data-driven decision-making.

I am a Data Engineer with over 5 years of experience in designing, developing, and optimizing data pipelines and cloud-based data architectures. My expertise lies in Big Data technologies such as Hadoop, Spark, and Kafka, which I leverage to manage and process large-scale datasets efficiently. I am proficient in cloud platforms like AWS and Azure, where I build scalable, cloud-native data solutions that meet the needs of modern businesses.

Throughout my career, I have successfully led the design and implementation of high-performance data pipelines, optimizing ETL workflows to significantly reduce processing times and improve data throughput. My role at Intel Corporation as a Senior Data Engineer allowed me to spearhead initiatives that resulted in a 40% reduction in processing time and a 15% decrease in cloud storage costs. I am passionate about data quality and governance, ensuring that all projects achieve high accuracy and compliance with industry standards.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

Intel Corporation

Jan 2022 - Present (3 years 4 months)

Spearheaded the design and implementation of highly scalable data pipelines, processing over 500TB of data per month. Optimized ETL workflows using Apache NiFi and PySpark, reducing processing time by 40% and improving data throughput. Led the migration to AWS, reducing cloud storage costs by 15% while improving data scalability.

Data Pipelines ETL Apache NiFi Pyspark AWS Data Modeling Apache Kafka Jenkins Docker Data Quality

Data Engineer

Goldman Sachs Global

Sep 2019 - Dec 2021 (2 years 3 months)

Designed and implemented end-to-end data pipelines for processing over 100 TB of daily transactional data. Leveraged Apache Spark and Hadoop to process big data, reducing processing time by 30% and improving efficiency. Led the migration of ETL processes to AWS Glue, resulting in 20% cost reduction on cloud computing resources.

Data Pipelines Apache Spark Hadoop ETL AWS Glue SQL NoSQL Snowflake BigQuery Data Governance Agile

Data Engineer (Internship)

Intel

May 2019 - Aug 2019 (3 months)

Assisted in building data pipelines to process and analyze large datasets, handling up to 10TB of data. Supported the integration of ETL processes using PySpark and SQL for data extraction, transformation, and loading. Assisted in maintaining and optimizing SQL Server databases, ensuring 99.9% data accuracy during data processing.