Data Platforms Engineer
£80,000 – £90,000 + benefits
Flexible working (1 day in the office per week)
Over the past few years, we’ve scaled the global Customer Experience Team (a hybrid startup-consultancy) in one of the world’s most powerful brands. The team encompasses Product, Strategy, Design, Analytics and Implementation. They design and deliver experiences for customers – connecting the customer’s devices with the brand and world around them.
With over 150,000 employees spread across almost 200 countries, our client has innovation at their core and is proud to be building products and services that leave a positive and sustainable impact on society, the environment and in education.
They are an organisation that enrich lives with a cross-functional, international environment built upon transparency and empathy. With almost 40 nationalities in the UK HQ, they embrace diversity and encourage applications from mixed backgrounds, genders, nationalities, ages and lifestyles – seeking to learn from these different perspectives.
Our client is moving towards the next phase of customer experience – evolving to delivering integrated journeys that meet customer needs traversing both online and offline channels.
The Data Platforms Engineer will design, implement, and maintain the data analytics platform infrastructure. This role will work with data science & analytics platforms, MLOps, ML operationalisation to enable efficient data analysis and machine learning operations.
As Data Platforms Engineer, you will:
- Collaborate with Data Scientists and Engineers to understand analytics and machine learning requirements. Translate these into platform design and implementation.
- Design and architect the data analytics platform infrastructure, taking into consideration scalability, performance, and security requirements.
- Install, configure, and deploy the data analytics and data science platform, MLOps setup, and other technologies to create a robust data analytics environment.
- Maintain documentation related to the data analytics platform infrastructure, including system architecture, installation procedures, and operational guidelines.
- Monitor platform performance and ensure the smooth operation of the platform, performing routine maintenance tasks, identifying bottlenecks, troubleshooting issues, and implementing upgrades and patches. Propose & implement optimisations to enhance system efficiency & reliability.
- Collaborate with Data Engineers and Data Scientists to integrate various data sources into the platform, ensuring data quality & accessibility for analytics & machine learning workflows.
- Be responsible for Data Virtualisation layers, covering data engineering activities where needed.
- Operationalise machine learning models, deploying these on the platform, creating APIs, and establishing monitoring mechanisms.
- Develop automation scripts and workflows, streamlining repetitive tasks and enhancing the efficiency of analytics processes.
- Optimise the platform’s performance by tuning system config, monitoring resource utilisation, and implementing performance-enhancing techniques.
- Implement security measures, adhering to governance policies that protect data assets – ensuring compliance and maintaining data privacy.
- Oversee governance activities managed by the data platform. Collaborate with cross-functional teams, including IT, security, and compliance to ensure alignment with global standards and policies.
- Provide support and guidance to platform users, addressing queries and assisting with troubleshooting.
- Stay updated with industry trends and emerging technologies in data analytics and machine learning, evaluating their potential impact and recommending relevant enhancements or additions.
We would like you to have
- Proven experience as a Data Platforms Engineer in an enterprise environment.
- Experience with machine learning platforms such as SageMaker, VertexAI, or Domino Data Lab. Demonstrable experience in setting up and managing data analytics platforms.
- Good knowledge of cloud platforms such as AWS, Azure, or GCP. Experience with both AWS & GCP is beneficial.
- Strong Python and SQL along with experience in scripting and automation is essential. R or Scala is beneficial.
- Strong knowledge of MLOps principles and best practices, including model deployment, version control, and monitoring.
- ML operationalisation tool and framework experience such as Docker, TensorFlow Serving, or Kubernetes.
- Knowledge of data integration, ETL / ELT processes, and data pipeline frameworks.
- Familiarity with security and governance principles in data analytics, including data privacy regulations and access controls.
- Excellent problem-solving, communication, and collaboration skills are essential.
- The chance to develop your career with a global, multicultural team working on a fascinating customer experience transformation programme.
- A flexible working environment and the ability to work from home / flexible hours.
- Private healthcare and private dental insurance.
- Competitive pension, 26 days holiday (excluding bank holidays).
- Car lease scheme, season ticket loan and cycle to work schemes.
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