Platform Engineer

Who we're looking for

A data platform engineer with cloud engineering skills. You'll be able to help us establish and integrate a variety of cutting edge technologies, into a coherent set of data platform services to serve our data practitioners including data engineering and data science. This new suite of data platform services will also underpin a number of exciting strategic initiatives.

You'll be a platform engineer, with experience of infrastructure-as-code fundamentals, ability to code in python, but also familiar dealing with occasional DSLs and configuration work required to make a platform work well and seamlessly.

Familiarity with adjacent technologies such as Airflow, docker/kubernetes, and exposure to a wide variety of data technologies would enable you to contribute to creating these services that others will use to meet their goals.

You will need to be able to quickly pick up and run with new technologies, to meet the varying needs of the team, while maintaining high quality well documented code. It would be a strong advantage if you've worked as, or alongside data engineers, and understand their particular needs from a platform perspective, with an eye for those touches that elevate a set of technical services into a coherent, elegant suite that interoperate well, and can be easily assembled into patterns. These patterns will become our "paved pathways" enabling greater productivity, and delighting practitioners by giving them a head-start in achieving their goals.

As an established platform engineer, you'll need to have a solid practical understanding of how to formulate, configure, integrate and test the platform, and are ideally from a computer science or applied science/engineering background, although relevant industry experience is equally valuable and will be taken into consideration. This is an exciting opportunity to really shape and develop this offering, and be a part of not just the implementation, but the conception of how best to create this next generation platform.

About Schroders

We're a global investment manager. We help institutions, intermediaries and individuals around the world invest money to meet their goals, fulfil their ambitions, and prepare for the future.

We have around 5,000 people on six continents. And we've been around for over 200 years, but keep adapting as society and technology changes. What doesn't change is our commitment to helping our clients, and society, prosper.

The base

We moved into our new HQ in the City of London in 2018. We're close to our clients, in the heart of the UK's financial centre. And we have everything we need to work flexibly.

The team

Enterprise Data Platform Engineering is a new team of platform engineers embedded in Global Technology in the Enterprise Data area. Our goal is to knit together a wide variety of services to produce a differentiated and user-friendly service proposition for our data practitioners to use in pursuit of their business objectives.

We use a modern data and technology stack including Python, Docker/Kubernetes, data virtualisation (Denodo), Snowflake and AWS cloud based stacks, and are hands on, partnering closely with both business subject matter experts and technology counterparts to deliver data solutions collaboratively.

What you'll do

You'll work with the team to help rationalise and integrate a variety of data technologies. The critical skill will be in understanding the tasks that data engineers and data scientists are trying to achieve, and formulate a sensibly integrated suite of platforms and tools, with automation at the heart. The services will tie together formerly disparate processes into blocks that can be assembled into patterns to form an effective and efficient process. You will help us define and implement some of those initial patterns. We will achieve this through careful listening to the voice of our customers, and adopt a highly iterative approach to rolling out these services focusing on early business value.

This might involve integrating a new ELT tool with Snowflake, working with CI/CD pipelines to enable teams to achieve transformation, metadata management and serving of data, along with useful guides to onboard users and teach useful patterns.

The stack currently consists of data virtualisation, Snowflake, and AWS tools but will extend to a strategic multi-cloud (Azure/AWS) offering that will allow data engineers and data scientists to serve the business more rapidly.

This team does not operate in isolation- but collaborates with our data engineering and data acquisition team, focussing on the data technology, integration and platform engineering aspects.

It's an opportunity to get exposure to a broad variety of technology, that will ultimately support:

• A strategic migration of enterprise applications and analytics to Snowflake (with the help of consultant systems integrators for the duration of the migrations)
• The creation of a group wide Analytics Data Warehouse
• The evolution of a Data Science Platform in partnership with our Data Insights teams
• A variety of supporting services such as ELT platform, and analytics engineering infrastructure (DBT and similar)

You'll be comfortable with agile methodology, either scrum or Kanban, having worked in teams that use this approach. As a team, we support each other in our personal development knowing that each has their strengths, and work to share those throughout the team. We hope you'll have something to offer and something to gain.

The knowledge, experience and qualifications you need

• Solid Python skills
• Experience standing up data platforms, either open source, or integration of 3rd party cloud or SaaS services.
• Familiar with data virtualisation and APIs.
• Experience of infrastructure as code tools such as Terraform
• CI/CD / general dev-ops experience.
• Familiar with a variety of cloud native data stores
• Cost management on cloud
• Hybrid architectures (Cloud/on premises) and multi-cloud.
• Good knowledge of agile methodology, and capable of following the framework, contributing to team success through participation in ceremonies and occasionally assisting with scrum-master duties, owning of retro actions and maintaining scrum artefacts.
• Good knowledge of source control (GitHub especially) and working on a shared code-base.
• Familiarity with security controls and experience interacting with information security teams to ensure the solution is suitably secured and controlled.

The knowledge, experience and qualifications that will help

• Educated to undergraduate or higher level in a numerate or applied science subject, but relevant industry experience will be equally valuable, and considered.
• AWS data engineering (S3, Glue etc), Snowflake or Denodo certifications.
• Experience with the Denodo data platform including cache management, scheduling, performance and tuning of queries
• Experience with Data Warehousing technology stacks.

What you'll be like

• Friendly, approachable and a collaborative team player who enjoys working with people across the business and can act as a mentor to more junior colleagues when required.
• Having a continuous improvement mind-set, always thoughtful about the status quo, and making sure that standard approaches continue to make practical sense.
• Self motivated, someone who shows initiative often and is keen to help the team improve engineering processes across Schroders.
• A continuous learner - always willing to spend time learning and developing your technical skills on our current tool suite and related disciplines such as data modelling and architecture.
• A problem solver, comfortable analysing, breaking down and ultimately resolving complex and sometimes ambiguous requirements.
• Down-to-earth, honest and straightforward, you'll be able to stand your ground and communicate opposing ideas, without being confrontational

We're looking for the best, whoever they are

Schroders is an equal opportunities employer. You're welcome here whatever your socio-economic background, race, sex, gender identity, sexual orientation, religious belief, age or disability.