Data Analyst
Iasi, Romania
1 zi în urmă
source : Just Join IT

Python (regular)

Data Visualization (advanced)

Data analysis (advanced)

Statistics (advanced)

About us :

edrone is an AI-fueled SaaS platform, providing Customer Experience solutions for eCommerce. It is used by hundreds of SMEs online retailers in CEE, Southern EU, and LatAm.

edrone won multiple awards, including Computer World’s "Best in Cloud" in 2017 and 2018. TECH5 by TNW, 2nd place in commerce software worldwide competition 2022 by G2.

Responsibilities :

  • Close collaboration with Product Team
  • Preparing reports and analyses
  • Drawing conclusions and building recommendations from your findings
  • Automating update and the delivery of reports
  • Working inside Data Scientist Team
  • Skills - required :

  • min. 2 years of experience in field
  • Data Analytics
  • the ability to present the results transparently and clearly
  • focus on giving value to business
  • knowledge of A / B tests
  • analytical skills
  • data modelling
  • experience in Relational databases
  • ability to perform complex selects
  • preferably MySQL, presto
  • Practical knowledge of BI Tools (AWS QuickSight is preferred)
  • Experience in Python
  • Basic knowledge of GIT
  • Skills - nice to have :

  • AWS (Athena, S3, Lambda, RDS, DynamoDB, QuickSight)
  • Google Analytics
  • Basic knowledge of data processing pipelines
  • How we work :

  • DevOps - you build it you run it
  • Small, tightly-knit groups of very skilled people
  • Code Reviews
  • Directly Responsible Individual
  • Pair programming
  • Paying back technical debt whenever you can
  • 1 : 1s
  • Blameless postmortems
  • What we value :

  • Seeking mastery. We read books, attend conferences and meetups. We have a library of books. We study alone and in groups.
  • Company has a budget to support us. We do this because it is our passion.

  • Curiosity. If we use something we want to know how it works exactly. What are the constraints, when does it fail
  • Direct, honest and timely feedback. This is how we improve.
  • Autonomy. We support each other and we actively avoid micromanagement.
  • Being a good human. We don’t tolerate jerks, no matter how brilliant they are
  • Interesting technical challenges :

  • Scalability - we ingest tons of data that must be processed near-real time. Traffic patterns change constantly and we have to adapt dynamically.
  • We rely on horizontal partitioning and auto-scaling a lot.

  • Reliability - uptime, latencies, queue processing delays - we live and breathe by these metrics. We assume machines, disks, network and software will fail.
  • Our approach is resilience engineering and automation.

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