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This fast-growing cybersecurity startup specialises in identifying and interpreting exposed data, leveraging an AI SaaS platform to analyse corporate data in near real-time. They provide customers with assurance and reduce risks associated with exposed data across complex supply chains.
The Role
- Design, build, and operate batch and streaming data pipelines to turn raw exposed data into queryable intelligence.
- Own end-to-end data pipelines, including ingestion, transformation, modelling, and serving data at scale.
- Support analytics, machine learning models, and customer-facing platforms with reliable data.
- Work closely with founders, platform, and software engineers in an international team.
- Act as the go-to expert for data movement within the company, ensuring data quality, correctness, and scalability.
- Contribute to ML efforts through data cleaning and feature engineering for training pipelines.
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What You'll Need
- Demonstrable experience designing, building, and operating production data pipelines for both batch and streaming.
- Strong Python for data engineering, including hands-on PySpark at scale (AWS EMR).
- Proficiency in SQL for modelling, transforming, and querying large datasets.
- Experience with streaming data using Kafka and ingesting large, heterogeneous, unclean data from various sources.
- Knowledge of Lakehouse and data warehousing patterns (Apache Iceberg, Athena, S3, columnar formats like ORC) and workflow orchestration with Airflow.
- Experience with OpenSearch, Infrastructure as Code (Terraform), managing core AWS services, and building CI/CD pipelines (GitHub Actions).
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What's On Offer
- Opportunity to join a fast-growing company at an early stage with significant contribution potential.
- Competitive salary based on skills and experience.
- Company pension scheme.
- Eligibility for a company share through an options grant after a qualifying period.
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