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# Data Engineer

- **Company:** White Circle
- **Location:** London, UK
- **Salary:** £60,000.0 - £80,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Amazon S3, Customer Data Management, Web Scraping, Data Infrastructure, Extract Transform Load (ETL), Data Warehousing, Relational Databases, Python (Programming Language), PostgreSQL, Operational Databases, Standard Sql, Data Streaming, Management of Software Versions, Event Driven Architecture, Apache Kafka, Data Pipelines
- **Published:** July 10, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=06e2fd9860d1c1ab

## About the Role

* Solid experience as a data engineer building and running production pipelines and data warehouses.
 * Strong SQL and Python, and comfort designing data models that other people build on.
 * Hands-on experience with a modern data stack: relational databases, a columnar/analytics store, orchestration, and transformation tooling.
 * You've worked with PostgreSQL (or similar) and understand how to structure and query data efficiently.
 * A production mindset - you care about reliability, migrations, performance, and cost, not just getting a query to return.
 * Able to work independently and own problems end-to-end in a fast-moving, early-stage environment.
 * You communicate clearly and can work in English.

Nice to have / Big plus

 * Experience with streaming/eventing (e.g., Kafka).
 * Experience versioning datasets and building lightweight data pipelines.
 * Experience with dbt for data modeling / analytics engineering, with modern ETL tools (Fivetran, Airbyte, dlt)
 * Experience with AWS (Athena, Glue, S3, etc.).
 * Familiarity with GTM/CRM data workflows and enrichment tools (Crunchbase, Clay).
 * Some web-scraping experience.
 * Comfort with a systems language (we use Rust for internal data tooling and SDKs).

## Description

* Design, build, and maintain production data pipelines - ingestion, transformation, and orchestration - that are reliable enough to be depended on.
 * Model and structure data in the warehouse so it's clean, documented, and genuinely useful to engineering, product, and research teams.
 * Own data infrastructure alongside the current data engineer: schema design, migrations, performance, and cost.
 * Provide data support to GTM engineers - deliver curated, enriched company and account data and the modeling layer that makes it query-ready.
 * Integrate and route diverse data sources into the warehouse, from internal product events to third-party enrichment feeds.
 * Occasionally build data-extraction jobs, including web scraping, when a source isn't otherwise available - one task among many, not the core of the role.
 * Improve data quality, observability, and documentation so the team can move quickly without breaking things.
 * Diagnose and fix pipeline issues before they become someone else's problem.
 * Jump into data and infra tasks where needed and make things more robust.


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