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# Data Engineer

- **Company:** Lambda World
- **Location:** Greater London, UK (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Data Analysis, Big Data, Cloud Computing, Continuous Integration, Data Governance, Data Transformation, Data Stores, Data Warehousing, Relational Databases, DevOps, PostgreSQL, Machine Learning, NoSQL, NumPy, Apache Oozie, Open Source Technology, Performance Tuning, Software Engineering, Feature Engineering, Apache Spark, Infrastructure as Code (IaC), Data Lakes, Apache Flink, Data Analytics, Xgboost, Apache Kafka, Machine Learning Operations, Databricks
- **Published:** August 30, 2026
- **Apply:** https://www.collegerecruiter.com/job/2815437983-data-engineer

## About the Role

+ Kafka
 + Flink
 + Spark
 * Databricks experience is a plus
 * Experience implementing:
 + Data Lakes
 + Big Data Analytics platforms
 * Cloud-based solutions experience
 * AWS preferred

Software Engineering

 * Expertise with:
 + RDBMS
 + Data Warehousing
 + Data Modelling
 * Experience with:
 + Redshift
 + PostgreSQL
 + NoSQL databases

DevOps & Infrastructure

 * Proficiency in:
 + DevOps practices
 + CI/CD pipeline management
 + Infrastructure as Code (IaC), + Data Quality
 + Data Governance techniques
 * Knowledge of:
 + MLOps
 + Feature Engineering
 * Exposure to data analysis and ML tools such as:
 + scikit-learn
 + pandas
 + NumPy
 + XGBoost
 + LightGBM
 * Familiarity with orchestration tools:
 + Apache Oozie
 + Apache Airflow

## Description

* Shape the architecture of data products designed for:
 + data analytics
 + forecasting
 + feature engineering
 + customer behavior analysis
 + integration of new data sources

Data Transformation & Governance

 * Lead best practices in:
 + data modelling
 + performance optimization
 + data governance
 * consistent
 * available
 * reliable
 * Build reusable technology enabling teams to:
 + ingest data
 + store data
 + transform data
 + serve their own data products

Collaboration & Product Thinking

 * Work closely with:
 + data scientists
 + machine learning engineers
 * Refine data requirements and evolve infrastructure capabilities
 * Stay updated with emerging technologies
 * Experiment with open source tools
 * Participate in community-building initiatives such as meetups
 * Continuously learn and improve engineering practices


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