Nikhil Jatale

I embarked on my data engineering journey in 2017, and it's been an exhilarating four-year adventure. During this time, I've played a significant role in the digital landscape, contributing to impactful software solutions that have left a lasting mark. I take pride in maintaining a 99% data pipeline uptime, demonstrating my dedication to ensuring smooth data flow. Additionally, I've achieved a notable 22% increase in efficiency KPIs, showcasing my knack for optimizing processes.

However, my journey goes beyond my professional achievements. Outside of work, I've taken on personal projects that have pushed boundaries. These include developing a real-time sign language recognition system and creating a data pipeline for processing S&P 500 data. My dedication to excellence has been recognized with two "Employee of the Month" awards. To stay at the forefront of industry trends, I've completed multiple data science and machine learning courses.

My technical expertise encompasses Python, R, PostgreSQL, MS-SQL, MongoDB, TSQL, and various data-related tools. I proficiently manage data warehousing, integration, modeling, visualization, and analysis. Furthermore, I possess skills in Apache Spark, PySpark, Kafka, Airflow, Git, GitHub, EC2, Redshift, IAM, S3, Glue, Kinesis, Docker, Tableau, Power BI, and various DevOps tools, making me a versatile data engineer.

I have a Bachelor of Engineering degree in Computer Science, which provided me with a solid technical foundation. Additionally, I hold a Master of Science (MSc) in Big Data Science from Queen Mary University of London, which has honed my expertise in the field of big data and data science. This combination of educational qualifications has significantly contributed to my knowledge and skills in the data engineering and analytics domain.