Data Platform Engineer

📍 Job Overview

• Job Title: Data Platform Engineer
• Company: Policy Expert
• Location: London, England, United Kingdom
• Job Type: Hybrid (3 days on-site, 2 days remote)
• Category: Data Engineering
• Date Posted: August 8, 2025

🚀 Role Summary

• Key Responsibilities:
  • Define and implement best practices in infrastructure and software delivery in GCP environments.
  • Evolve CI/CD tooling using GitHub Actions to enable fast, reliable deployments.
  • Build reusable Infrastructure-as-Code (IaC) templates and pipelines for product and data teams to build on.
  • Develop automation and tooling to improve developer productivity and operational efficiency.
  • Provide expert advice and guidance to other developer teams on cross-functional aspects, identifying performance bottlenecks, and ensuring optimal scalability.
  • Embrace cloud cost optimization, observability, and high-quality infrastructure delivery practices.
  • Leverage the latest technologies and tools to enhance developers' productivity and overall platform experience.

💻 Primary Responsibilities

• Responsibilities:
  • Define and implement best practices in infrastructure and software delivery in GCP environments.
  • Evolve CI/CD tooling using GitHub Actions to enable fast, reliable deployments.
  • Build reusable Infrastructure-as-Code (IaC) templates and pipelines for product and data teams to build on.
  • Develop automation and tooling to improve developer productivity and operational efficiency.
  • Provide expert advice and guidance to other developer teams on cross-functional aspects, identifying performance bottlenecks, and ensuring optimal scalability.
  • Embrace cloud cost optimization, observability, and high-quality infrastructure delivery practices.
  • Leverage the latest technologies and tools to enhance developers' productivity and overall platform experience.

🎓 Skills & Qualifications

Education: Relevant degree in Computer Science, Engineering, or a related field with a strong focus on data engineering and infrastructure.

Experience: Proven experience in data engineering, infrastructure management, or a related role with a strong focus on cloud platforms, preferably GCP.

Required Skills:

• Hands-on GCP experience, especially in infrastructure or data platform contexts (e.g., BigQuery, Cloud Functions, IAM, etc.).
• Proficiency in at least one IaC framework; experience in Terraform would be ideal.
• Strong focus on automation and experience with modern CI/CD tooling (e.g., GitHub Actions, GitLab CI, CircleCI).
• Proficiency in Python with a strong emphasis on scripting in a test-driven, object-oriented manner.
• Experience with AWS cloud infrastructure is a bonus, as there could be opportunities to contribute to the existing AWS estate.
• A mindset of treating infrastructure as software, with a focus on code reviews, trunk-based development, and automated testing.

Preferred Skills:

• Experience with observability practices and tooling such as logging, alerting, and tracing.
• Cost awareness and ability to manage cloud spend efficiently.
• Ability to influence technical initiatives and reinforce best practices.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

• Specific data engineering projects showcasing GCP infrastructure, automation, and CI/CD tooling.
• Projects demonstrating proficiency in Python scripting and IaC frameworks, preferably Terraform.
• Case studies highlighting performance optimization, cost management, and scalability improvements.

Technical Documentation:

• Code quality, commenting, and documentation standards for data engineering projects.
• Version control, deployment processes, and server configuration documentation.
• Testing methodologies, performance metrics, and optimization techniques for data engineering projects.

💵 Compensation & Benefits

Salary Range: £45,000 - £60,000 per annum (region-appropriate for London, based on web development and data engineering industry standards)

Benefits:

• Pension contributions matched up to 7%.
• Private medical and dental cover.
• Learning budget of £1,000 a year + study leave.
• Enhanced maternity and paternity leave.
• Travel season ticket loan.
• Access to a wide selection of London O2 events and use of a private lounge.
• Employee wellbeing programme.
• Prayer room in the office.

Working Hours: 40 hours per week, with flexible deployment windows and maintenance schedules.

🎯 Team & Company Context

Company Culture:

• Industry: Insurance technology, focusing on data-driven home, motor, and pet insurance products.
• Company Size: Medium-sized, with a growing team of over 150 employees.
• Founded: 2011, with a strong focus on innovation and continuous improvement.

Team Structure:

• The DataOps team ensures the data platform is reliable, secure, and high-performing, managing tools, infrastructure, and standards for data products and services.
• The team works closely with product and data teams to build and maintain trusted data products with speed and confidence.
• The DataOps team consists of experienced data engineers, infrastructure specialists, and software developers.

Development Methodology:

• The company follows Agile methodologies, with a focus on sprint planning, code review, testing, and quality assurance practices.
• Deployment strategies include CI/CD pipelines and automated deployment tools.
• The company embraces continuous integration, continuous delivery, and infrastructure as code (IaC) principles.

Company Website: https://www.policyexpert.co.uk/

📈 Career & Growth Analysis

Web Technology Career Level: Mid-level data engineering role with a strong focus on cloud infrastructure, automation, and CI/CD tooling.

Reporting Structure: Reports directly to the DataOps Team Lead, working closely with the Head of Data Engineering and other data engineering teams.

Technical Impact: Responsible for defining and implementing best practices in GCP environments, ensuring optimal scalability, and enhancing developers' productivity.

Growth Opportunities:

• Technical Growth: Develop expertise in GCP infrastructure, data engineering, and cloud cost optimization.
• Leadership Potential: Potential to mentor junior data engineers and contribute to technical decision-making processes.
• Architecture Decisions: Opportunities to influence architecture decisions and contribute to the design of large-scale data platforms.

🌐 Work Environment

Office Type: Hybrid, with a modern, collaborative workspace in central London.

Office Location: 10 Finsbury Square, London EC2A 1AF, United Kingdom

Workspace Context:

• The office features an open-plan layout, promoting collaboration and communication among team members.
• The workspace includes dedicated areas for quiet work, team meetings, and social events.
• The office is located in the heart of London, with easy access to public transportation and nearby amenities.

Work Schedule:

• Working Hours: 9:00 AM - 5:30 PM, Monday to Friday, with a one-hour lunch break.
• Hybrid Work Arrangement: 3 days on-site, 2 days remote, with flexible working hours and remote work guidelines.
• Deployment Windows: Flexible deployment windows and maintenance schedules, with on-call responsibilities as needed.

📄 Application & Technical Interview Process

Interview Process:

1. Phone Screen: A brief call to discuss your experience, skills, and career goals (30 minutes).
2. Technical Assessment: A hands-on assessment focusing on data engineering tasks, GCP infrastructure, and automation (2 hours).
3. On-site Interview: A face-to-face interview with the DataOps Team Lead, Head of Data Engineering, and other team members, focusing on technical skills, cultural fit, and problem-solving abilities (1.5 hours).
4. Final Decision: A final decision based on the interview performance, technical assessment, and cultural fit.

Portfolio Review Tips:

• Highlight specific data engineering projects showcasing GCP infrastructure, automation, and CI/CD tooling.
• Emphasize performance optimization, cost management, and scalability improvements in your projects.
• Prepare a live demo of your portfolio, focusing on user experience, technical implementation, and responsive design.

Technical Challenge Preparation:

• Brush up on your GCP infrastructure knowledge, focusing on BigQuery, Cloud Functions, IAM, and other relevant services.
• Familiarize yourself with modern CI/CD tooling, such as GitHub Actions, GitLab CI, and CircleCI.
• Practice coding challenges and algorithm problems to improve your problem-solving skills and technical proficiency.

ATS Keywords: (Comprehensive list of web development and data engineering-relevant keywords for resume optimization, organized by category)

• Programming Languages: Python, Bash, SQL, JavaScript
• Web Frameworks: Flask, Django, FastAPI
• Server Technologies: GCP (BigQuery, Cloud Functions, IAM), AWS (EC2, RDS, Lambda), Docker, Kubernetes
• Databases: PostgreSQL, MySQL, MongoDB, Redis, BigQuery
• Tools: Terraform, Ansible, Jenkins, Git, GitHub, GitLab, CircleCI, JIRA, Prometheus, Grafana, ELK Stack, AWS CloudWatch, Google Cloud Logging
• Methodologies: Agile, Scrum, Kanban, CI/CD, IaC, DevOps, Infrastructure as Code, GitOps, GitFlow, Trunk-based development
• Soft Skills: Problem-solving, Troubleshooting, Debugging, Communication, Teamwork, Leadership, Mentoring, Technical Writing
• Industry Terms: Data Engineering, Cloud Engineering, Infrastructure as Code, CI/CD, DevOps, Data Platform, Data Pipeline, Data Warehouse, Data Mart, ETL, ELT, Data Transformation, Data Integration, Data Governance, Data Quality, Data Security, Data Privacy, Data Compliance, Data Lineage, Metadata Management, Data Catalog, Data Dictionary, Data Glossary, Data Dictionary, Data Lake, Data Mesh, Data Fabric, Data Governance, Data Stewardship, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data Custodian, Data 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