Quantiphi
Quantiphi
Quantiphi ir mākslīgajam intelektam prioritāti piešķirošs digitālās inženierijas uzņēmums, kas palīdz organizācijām izmantot mākslīgo intelektu, datus un mākoņtehnoloģijas sarežģītu uzņēmējdarbības problēmu risināšanai. Uzņēmums, kas dibināts 2013. gadā, apvieno konsultāciju un inženierijas kompetenci, lai izstrādātu mērogojamas mākslīgā intelekta lietotnes, attīstītu datu analītikas iespējas, modernizētu mākoņinfrastruktūru un īstenotu pielāgotus risinājumus. Tā darbs atbalsta organizācijas veselības aprūpes, finanšu, izglītības, mazumtirdzniecības un loģistikas nozarēs. Tehnoloģiju partneru vidū ir AWS, Google Cloud un NVIDIA. Quantiphi komandas koncentrējas uz progresīvu tehnoloģiju pārvēršanu praktiskos risinājumos uzņēmumu pārveidei.

Senior Data Engineer, Onsite in India

Senior Data Engineer responsible for Azure pipelines, hybrid cloud architectures, and distributed machine learning workflows. Contribute to large-scale data modernization at Quantiphi.

Apraksts

  • Help plan and implement data flows, including sizing and configuration
  • Support Solution Architects in transforming and modernizing Azure-based data solutions
  • Develop production-ready code for distributed machine learning workflows
  • Design and implement Azure data pipelines and streaming data flows
  • Build hybrid data solutions that integrate on-premises and cloud services
  • Investigate complex distributed-systems issues and deliver scalable solutions
  • Create tools for ingesting and processing terabytes of data each day
  • Collaborate with product owners, analysts, architects, platform engineers, and cloud vendors
  • Prepare detailed designs, technical specifications, and test plans
  • Own development tasks, production releases, and post-production support
  • Assist with unit, system integration, and user acceptance testing
  • Coordinate testing and migration activities with business and support teams

Prasības

  • 4–7 years of professional experience
  • Currently serving a notice period of 0–15 days
  • Strong hands-on experience with Spark and PySpark
  • Advanced SQL skills, including query performance optimization
  • Strong practical experience with Python, Java, or Scala
  • Experience working with Azure, AWS, or Google Cloud Platform
  • Experience with Azure Data Factory is advantageous
  • Databricks experience is desirable
  • Experience with Snowflake or Synapse data warehouses
  • Ability to design dimensional models for data warehouses and marts
  • Knowledge of version control and build automation tools, including Azure DevOps and/or MLOps
  • Understanding of operating system internals, file systems, disk and storage technologies, and storage protocols

Priekšrocības

  • A culture centered on transparency, diversity, integrity, learning, and growth
  • An environment that supports innovation and both professional and personal development

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