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# Data Engineer

- **Company:** Burq, Inc.
- **Location:** UK (Remote available)
- **Experience:** Experienced
- **Salary:** £50,397.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Microsoft Azure, BigQuery, Cloud Computing, Cloud Database, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Python (Programming Language), Machine Learning, SQL Databases, Data Streaming, Workflow Management Systems, Snowflake, Build Management, Apache Kafka, Operational Systems, Web Api, Programming Languages
- **Published:** October 25, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5358961968

## About the Role

* Experience: 3+ years of experience in data engineering, preferably in a startup or high-growth environment.
 * Technical Skills:

 * Proficiency with SQL and at least one programming language (Python, Scala, or Java).
 * Experience with cloud data warehouses (Snowflake, BigQuery, or Redshift).
 * Familiarity with workflow orchestration tools (Airflow, Dagster, Prefect).
 * Hands-on experience with data streaming (Kafka, Kinesis) is a plus.

 * Mindset: A builder mentality-comfortable with ambiguity, fast iterations, and working in a small but mighty team.

## Description

As one of our first Data Engineers, you will be responsible for designing, building, and maintaining the pipelines and infrastructure that power our data-driven decision-making. You'll work closely with product, operations, and engineering teams to ensure that data is clean, reliable, and ready to drive insights, from optimizing delivery routes to improving customer experiences.

This is a unique opportunity to build scalable data systems from the ground up and shape the foundation of our analytics and AI capabilities.
What You'll Do

 * Design & Build Pipelines: Develop and maintain scalable ETL/ELT processes to ingest, clean, and transform data from multiple sources (internal systems, third-party APIs, IoT devices).
 * Data Modeling: Design and implement efficient data models for analytics, machine learning, and operational systems.
 * Infrastructure: Own the data infrastructure, leveraging cloud-native solutions (e.g., AWS, GCP, or Azure) and modern data tools.
 * Collaboration: Partner with data scientists, analysts, and software engineers to deliver data products that enable smarter decision-making.
 * Data Quality: Implement robust monitoring, validation, and governance to ensure accuracy, security, and compliance.

 * Scalability: Architect solutions that can handle rapid growth in data volume and complexity as the business scales.