Senior Data Engineer, Data Products & APIs
Hybrid London Full-time Senior Data Engineering
At a glance
Senior Data Engineering role at Kharon. London · full-time · £95,000–£115,000 base.
SalaryStated by Kharon
£95,000 – £115,000
Base salary for this role, in GBP per year, as published in the posting.
This role, from KharonOur estimate for the market: £86,250–£114,500Median £100,000
The range includes the market median of £100,000 for senior data engineering roles in London.
Worked out from 8 postings that stated pay, median £100,000. Last updated Oct 14, 2026.
RoleData Engineering
SenioritySenior
LocationLondon
WorkplaceHybrid
EmploymentFull-time
Base pay£95,000–£115,000
PostedAug 25, 2026 · 2mo ago
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Build and run pipelines and APIs that reconcile real-world entities, their connections and risk signals
- Create reusable Python libraries and APIs so engineering teams can access reconciled entity data centrally
- Move experimental machine-learning models and agentic systems toward production, with monitoring and evaluation
- Improve the technical architecture and maintain data quality through validation, documentation and testing
What you bring
- You have designed and deployed complex data systems, ideally involving NLP, search or graph analysis
- You bring strong Python skills, API ownership, and experience building libraries used by other engineering teams
- You can design relational schemas and improve query performance, with SQL and data-pipeline technology experience
- You communicate clearly and can explain architecture choices to technical and business audiences
Who this fits
This is a senior, full-time engineering role for someone comfortable working across data pipelines, APIs and architecture. You’ll work from London on a hybrid schedule, with three days each week in the office. The role suits an engineer open to an AI-assisted, agentic development workflow.
From the employer
TL;DR Kharon is seeking a full-time London-based Senior Data Engineer.
Location: London, UK Annual Remuneration: £95,000 - £115,000 salary and discretionary annual bonus Pay Frequency: Monthly Probationary Period: 180 days Work Arrangement: Hybrid, 3-days per week
Location: London, UK Annual Remuneration: £95,000 - £115,000 salary and discretionary annual bonus Pay Frequency: Monthly Probationary Period: 180 days Work Arrangement: Hybrid, 3-days per week
- Design, deploy, and maintain data-intensive pipelines and APIs that match real-world entities, relationships, and risk factors, including the deduplication and canonicalization logic behind corporate data, and the checks that validate the quality and consistency of that data.
- Build reusable Python packages and APIs, following software best practices, that expose reconciled entity data as a central service for other Kharon engineering teams.
- Help mature experimental ML models and agentic systems into production, including monitoring, evaluation harnesses, and version control.
- Take an active, big-picture role in driving improvement to Kharon’s technical architecture.
- Work at a fast, AI-assisted development pace (Claude, Cursor); prior LLM tool experience is not required, but comfort adapting to an agentic workflow is.
- Follow good engineering practices: documentation, diagrams, and unit/validation testing.
- Direct experience designing and deploying sophisticated data solutions, ideally including advanced models such as NLP, search algorithms, or graph analytics.
- Strong API design and ownership experience, ideally with FastAPI (Flask/Spring Boot acceptable).
- Strong Python fundamentals, including Pandas, PySpark, or Polars.
- SQL and relational database experience: schema design, access patterns, and query performance optimization.
- Experience designing and maintaining Python libraries used by other engineering teams.
- Experience with data pipeline technologies (Airflow, Kafka, AWS Glue, or equivalents).
- Strong verbal and written communication skills, with the ability to justify architectural decisions to both engineering and business stakeholders.
- Experience taking ML models from prototype to production, including monitoring, versioning, and evaluation.
- Experience with graph databases (Neo4j, Neptune) or graph-based data modeling.
- Familiarity with fuzzy-matching/entity-resolution libraries (Splink, Dedupe.io, Senzing, Zingg).
- Experience with Elasticsearch data and query modeling.
- Container-based deployment experience (Docker, Kubernetes).
- Interest or experience in geopolitics, sanctions compliance, or financial risk.
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