Data Platform Engineer

Code First Girls

. Design, deploy, and manage cloud-native data platform infrastructure across AWS, Kubernetes, and containerised environments .

Posted 9/17/2026full-timeLondon • United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and managing cloud-native data platforms using AWS and Kubernetes, with a strong focus on CI/CD pipeline development and data engineering practices. Proficient in applying Infrastructure-as-Code principles and ensuring data quality and platform reliability.

Highest-signal resume keywords

Production Python ProgrammingCloud CI/CD Pipeline DevelopmentData Engineering ExperienceApache Kafka and Apache FlinkAWS and Kubernetes Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Python ProgrammingData EngineeringApache SparkPySparkPandasAirflowInfrastructure-as-CodeData Quality PracticesReal-Time Data StreamingDistributed Processing

Soft Skills

CollaborationTechnical GuidanceProblem Solving

Tools & Technologies

AWSKubernetesDockerApache KafkaAWS KinesisApache Flink

Industry Keywords

Cloud-Native Data PlatformCI/CD PipelinesData WarehousingObservabilityIntelligent Automation

Tech Stack

Tools & technologies

AirflowApacheAWSCloudDockerKafkaKubernetesPandasPySparkPythonSpark

About the role

Key responsibilities & impact

Design, deploy, and manage cloud-native data platform infrastructure across AWS, Kubernetes, and containerised environments
Build and maintain CI/CD pipelines for automated testing, releases, and platform deployments
Manage infrastructure for real-time data streaming and distributed processing using Apache Kafka and Apache Flink
Apply Infrastructure-as-Code principles to manage platform security, optimize cloud resource usage, and control operational costs
Collaborate with data scientists, analysts, and business users to translate requirements into platform enhancements, documentation, and technical guidance
Explore and integrate AI-powered agentic capabilities, reusable prompt strategies, and intelligent automation features
Monitor data platform reliability, apply data quality practices, and resolve technical issues to ensure service resilience

Requirements

What you’ll need

Production Python programming skills, including building production-grade data applications, automation tools, and automated testing frameworks
Data engineering experience across distributed processing, data warehousing, and modern table formats
Experience with Apache Spark, PySpark, Pandas, and Airflow
Experience with AWS, Kubernetes/Amazon EKS, and Docker
Hands-on expertise building and maintaining cloud CI/CD pipelines
Understanding of data platform observability, monitoring, and data quality practices
Exposure to Apache Kafka, AWS Kinesis, or Apache Flink
Experience or strong interest in applied AI tools, Large Language Models (LLMs), prompt engineering, and agentic frameworks
Practical understanding of reusable prompt strategies and intelligent workflow automation and data discovery features

Benefits

Comp & perks

Free training course and education
No tuition fees, hidden fees, payment upon completion, or debt
Competitive client benefits (vary per client)