Hybrid London Data Engineer — Equity & Learning Budget

Nexalytica Ltd.

Greater London

Hybrid

GBP 70,000 - 110,000

Full time

6 days ago
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Benefits offered by this job

Learning budget (£2,500 annual)
Hybrid London office
Equity
Latest hardware and tooling
25 days annual leave + holidays
Private medical insurance
Pension scheme

Job summary

Nexalytica Ltd. is seeking a Data Engineer to design and maintain data infrastructure, build high-performance query layers, and develop database connectors for our platform. You will optimize pipelines and enable scalable data access for AI components, collaborating with AI/ML and frontend teams.

We value 3–5 years in data engineering, strong SQL across multiple engines, and Python expertise. The role is based in London with a hybrid work arrangement and offers a competitive package, learning

Qualifications

  • Bachelor's degree in Computer Science, Data Engineering, or related field.
  • 3–5 years of experience in data engineering or backend infrastructure.
  • Strong SQL proficiency and experience with multiple database engines (PostgreSQL, ClickHouse, BigQuery, MySQL).
  • Strong Python skills for scripting, tooling, and pipeline development.

Responsibilities

  • Design and maintain data infrastructure connecting our platform to customer databases.
  • Develop high-performance query execution layers and database connectors.
  • Optimize data pipelines and enable scalable data access for AI components.
  • Collaborate with AI/ML and frontend engineers to provide reliable data services.

Job description

Nexalytica Ltd. is seeking a Data Engineer to design and maintain data infrastructure, build high-performance query layers, and develop database connectors for our platform. You will optimize pipelines and enable scalable data access for AI components, collaborating with AI/ML and frontend teams.

We value 3–5 years in data engineering, strong SQL across multiple engines, and Python expertise. The role is based in London with a hybrid work arrangement and offers a competitive package, learning

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