Senior Data Engineer

4 weeks ago

United Kingdom CUBE Full time

Role: Senior Data Engineer

Location: UK (Remote-first with monthly travel to London required)


Recently listed as a "RegTech Top Performer" in Market Fintech's RegTech Supplier Performance Report, CUBE is pioneering the development of machine automated compliance.


We are a global RegTech business defining and implementing the gold standard of regulatory intelligence and change for the financial services industry. We deliver our services through a SaaS platform, powered by an innovative combination of AI and proprietary data ontology, to simplify the complex and everchanging world of compliance for our clients.


At CUBE, we are creating the future and are a company rooted in strong values, team spirit and commitment to our customers and wider communities. We serve some of the largest financial institutions globally and are expanding our footprint very fast. As we do so, we are keen for new talent to join us and realize their full potential to grow into leadership positions within the business.


Our Products:


RegPlatform is a technology platform that streamlines regulatory change management. It provides firms with a one-stop, continuously maintained inventory of global regulations, with effortless horizon scanning, integration capabilities and workflow management. RegPlatform combines industry leading AI technology with expert validated insights to simplify the complexities of multi-jurisdictional regulatory content.


RegBrain allows customers to apply CUBE's AI models directly to their own content, enabling faster release and feedback cycles. Our flagship AI services will be included, spanning structural detection, classification, entity extraction, summarisation, and recommendations. Available to customers and partners as APIs and via a UI.


Role mission:

  • Establish a strong data architecture in GCP for the backend of AI/ML/DS Services supporting a SaaS product.
  • Build cost-efficient, low latency data pipelines into and within GCP.
  • Represent the interests of the RegBrain AI team in the establishment of a global data strategy at CUBE.
  • Assist ML engineering team to build SOTA applications on structured and unstructured data, at scale, by providing fitted data solutions
  • Ensure seamless integration of RegBrain’s product features to analytics via CUBE’s data lake.
  • Work collaboratively with and continuously educate the ML team regarding best data practices including owning data governance.
  • Ensure reliability, completeness and quality of datasets replicated from internal sources.
  • Improve and manage ETL/ELT pipelines to the graph database(s) supporting CUBE's Knowledge Graph.


Responsibilities:

  • Create, orchestrate and monitor strong ETL pipelines for core datasets in the
  • business from on-prem to GCP.
  • Build monitoring solutions for data quality within GCP, with reporting to management stakeholders.
  • Support creation, orchestration and monitoring of data pipelines to graph database.
  • Support the establishment of a data catalogue.
  • Coordinate with the Infrastructure team to ensure seamless creation of needed solutions.
  • Manage data life-cycle and operational costs.


What we’re looking for:

  • Ideally, you will have exposure to GCP specifically and feel comfortable in technologies such as: BigQuery, DataFlow/Fusion/Proc, Pub/Sub, Composer.
  • If not, you must have worked within a complex, data-driven, SaaS environment and have the ability to learn quickly and adapt accordingly.
  • A specialist in their domain. You must be able to identify CUBE/RegBrain's needs, communicate planned solutions efficiently and carry them out.
  • You must be able to deal with: Data transport, NoSql, Caching, Message Buses, Data cleansing for textual data.
  • You not only merely think about data as bytes to be moved around, but as valuable information about the real world.
  • You are proactive. We will be relying on you to set the standards and raise them. Your work should enable others in the team.
  • An appetite for leadership. When the team grows from its success, you must be eager to assist its composition and lead more junior data engineers.
  • A can-do attitude. As a mid-size company, pragmatism is key. We need to enable short iteration cycles within the ML team while slowly establishing strong data foundations for its future growth.
  • You like working remotely as much as in-person.


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