Alex Doroshev
Engineering / Management / Consulting
16 years of experience in development, including 8 years leading teams of up to 30 people. I have developed distributed computing systems, data storage, analytics systems, and ML/GenAI systems of various scales in finance, marketing, b2b services and more
    • 16 years in Software Development
    • 12 years in Big Data Engineering
    • 8 years as a Tech Lead
    • Assembled multiple large scale teams in development and marketing
    • Built several Big Data systems and tools from scratch to production
    • Deep LLM understanding, and general experience in ML
    • Worked with clusters of up to 2000 nodes and 5 PB of data
    • Deep SQL/NOSQL databases knowledge
    • Experience with AWS, GCP, Databricks cloud providers
    • Immense experience building distributed systems
    • Profound domain expertise in Finance field
    • Understanding of modern design practices and workflows
    • Knowledge in marketing: performance, PR, community
(2022−2024) Own businesses / stealth startups
  • As a co-founder, I participated in creating a marketing business at the intersection of performance marketing and finance.
  • As a founding engineer, I am involved in creating a startup in the finance sector.
  • As a co-founder, I am involved in creating a startup in the GenAI field.
GPB Investments (Head of Development)
  • Built the engineering team from scratch, growing it to 30 skilled developers with primary focus on Java and React engineers.
  • Curated and actively participated in development of an in-house framework that ensures system stability, increases speed of feature implementations and improves code quality.
  • Led the overall development process, overseeing and guiding the team’s efforts.
  • Organized technical meetups and delivered lectures on Big Data to engage and involve the technical staff, mentored and guided engineers.
  • Led a team of around 10 Big Data Engineers and QA.
  • Collaboratively developed a Spark-based platform to process, analyze, and store customer data.
  • Constructed a serverless database leveraging Spark and the Parquet format, incorporating our own format for incremental data to enhance analytics capabilities.
  • Implemented services using AWS and GCP.
  • Optimized Spark and Cassandra for improved speed and cost efficiency.
  • Worked with a Hadoop cluster of 1500 nodes and HDFS with 5 PB of data.
  • Developed a Hadoop-based system to process 200 GB of new data from various sources daily.
  • Created different algorithms to process several TB of data per single Hadoop job.
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