Laban Okune Anunda
Software Engineer, Data Scientist, Tech Entrepreneur
About
The convergence of data science consulting and software engineering is increasing the opportunities of modern businesses to transform and improve their services. For many growing companies, reliable data infrastructure that can perform at scale is one of the biggest challenges today. I work with companies of all sizes to help implement the latest software and data science solutions.
I am pursuing a master’s degree in Data Science at the University of California, Berkeley. I currently lead a team at skyehi.tech that helps companies build robust software solutions and have previously co-founded a company providing crowdsourced traffic data in Kenya (Ma3Route). I have 15+ years of experience in the US and Kenya building highly impactful software solutions.
Originally from Kenya where I studied computer engineering, I moved to Southern California in 2015, then San Francisco in 2017 but recently relocated back to Kenya. I enjoy helping companies build out their data capacities so they can move beyond working in xls sheets.
EXPERIENCE
MileIQ Inc.
San Francisco, CA
Zendesk Inc.
San Francisco, CA
Kountable Inc.
San Francisco, CA
Compellon Inc.
Laguna Hills, CA
Ma3Route
Nairobi, Kenya
Kenya Medical Supplies Agency
Nairobi, Kenya
Alliance Technologies Ltd.
Nairobi, Kenya
Get in touch
I am available for consultancy projects and work collaborations. Reach out to laban.okune [at] skyehi.tech.
SKILLS
• Deep experience creating data models, building ETL pipelines, APIs and API integrations using Scala and Python
• Demonstrated ability to design and build APIs
• Specific frameworks used include: Reflex (Python), FastAPI, Akka HTTP, Http4s, Akka, Doobie, Reactive Mongo, Spring & Hibernate, Spark, HDFS
• Specific databases used include: PostgreSQL, MySQL, Neo4j, Elastic Search, Redis, MongoDB, Vitess
• Specific cloud engineering platforms used include: Amazon Web Services, Microsoft Azure, GCP, Confluent Cloud
• Exposure to R and working knowledge of applied statistics for data science