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Quantiphi Analytics

Posted 2 Nov 2026

Lead Data Engineer

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Employment
Full time
Team
Software Engineering
Posted
2 Nov 2026

About the role

Responsibilities:

  • Be a trusted technical advisor to customers and provide solutions for complex Cloud and Data-related technical challenges.
  • Be a thought leader in architecture design and development of cloud data analytics solutions.
  • Liaise with internal and external stakeholders to design optimised data analytics solutions on AWS cloud
  • Partner with SMEs and Solutions Architects from leading cloud providers to present solutions to customers
  • Support Quantiphi Sales and GTM teams from a technical perspective in building proposals and SOWs
  • Lead discovery and design workshops with potential customers across the globe
  • Design and deliver thought leadership webinars and tech talks alongside customers and partners.
  • Identify areas for productisation and feature enhancement for Quantiphi's product assets


Requirements:

  • Experience in designing, deploying, and operating highly available, scalable, secure, and fault-tolerant systems using Amazon Web Services (AWS) products like DMS, SCT, Redshift, Lambda, Glue, S3, EMR, Kinesis.
  • Strong experience in Data Migration using DMS and SCT services of AWS.
  • Strong Experience in migration and modernisation of databases and data warehouses on AWS cloud.
  • Good understanding of traditional databases and data warehouses like Oracle, Teradata, and Netezza.
  • Experience in developing ETL solutions using SSIS/Talend/Informatica.
  • Experience with Back-end engineering /API development using Python, etc.
  • Strong knowledge in OLTP/OLAP Systems, Dimensional Strategies - Star Schema, Snowflake Schema, and Ralph Kimball Methodology.
  • Ability to integrate 3rd party API programmatically. Ability to access AWS programmatically via its API.
  • Big data/Stream platform development experience with Kafka.
  • Write SQL code - Queries/Stored Procedures/Functions.
  • Previous experience working with distributed architectures like Hadoop, MapReduce, etc.
  • Understanding of DevOps best practices with CICD, Infrastructure as Code, etc.

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