AWS Data Engineer - Senior Level - Fintech - London - Up to £90,000
Flexible to remote and hybrid working.
Jefferson Frank are supporting a fintech consultancy applying innovative connected data approaches (graph databases, semantic and related technologies) to complex requirements for the financial services industry and NGOs.
The client has extensive experience in developing ESG data and software, leveraging the latest data science, big data, machine learning, data visualisation and financial research techniques. They are recognised as being at the forefront of applying these technologies to their work.
They are proud to be working on unique and impactful problems in the ESG and sustainability space, using a modern and varied tech stack.
They have built a relaxed, collaborative and innovative working environment and a great team of bright people at our London office in Kennington Lane. We embrace flexible working practices valuing both office based and remote working.
About the role
Looking for a mid-level or senior Data Engineer to join the team. The role will involve working with a wide range of financial and sustainability data to build connected datasets and tools to provide market leading insight into specific ESG issues in support of our products and consultancy work. High quality data engineering is a cornerstone of what we do and as such we are looking for someone with a demonstrated track record applying solid engineering principles to complex problems and data sets.
Candidates should have a strong computer science or equivalent technical background. Portfolio pieces illustrating Data Engineering and skills are strongly encouraged. It is useful, but not essential, to have experience using graph / semantic / linked data technologies, graphql, graph databases, Natural Language Processing (NLP) and machine learning techniques.
Knowledge of ESG and finance data is not necessary but an interest in the subject matter is useful.
* Developing complex ETL pipelines using Python and query languages such as SQL, GraphQL and Cypher.
* Wrangling data, standardising and conforming data to common data models
* Ensuring data quality and data integrity of our key datasets
* Managing resources on AWS and using those tools to run and schedule data processing and ingestion jobs
* Participating in Agile development activities, attending daily stand-ups, sprint planning, sprint review and sprint retrospective meetings
* Using SDLC systems (specifically Git, Jira, deployment models)
* Mentoring or providing technical guidance to colleagues and more junior team members.
* Contributing to wider project and product management practices contributing your technical expertise to help shape how the company approaches new challenges.
* Neural Alpha is a small tight-knit team and from time to time you may be asked to contribute to non-engineering activities such as business development or project management in support of your colleagues.
Experience (not all a prerequisite):
* Educational Background in Computer Science or alternative STEM degree
* 3+ years of commercial experience essential
* Preference for experience with: Python, GraphQL, Cypher, SQL. Evidence of problem solving capacity and engineering skills, just as important.
* Experience of working with corporate data e.g. in a financial crime analytics, AML / KYC environment
* Experience of data matching and entity resolution (e.g. addresses, names and companies)
* Experience of deploying Microservices
* Experience of modern development standards and best practices - TDD, CI/CD
* Experience of or interest in ESG data or sustainability/biodiversity data concepts
We are recruiting on the basis of 3 staffing models for this role:
* London based - 50 / 50 office & remote based.
* Hybrid - 1 day per week travel to the London office
* Fully remote in Europe with occasional travel to London
* Annual discretionary bonus scheme
* Flexible working practices with the ability to work remotely
* 25 days starting holiday
* Eligibility to company share options scheme after passing 6 month probationary period
1 Initial Interview
2 2nd Interview / Technical exercise
3 Final interview & exercise feedback
Email - email@example.com