The Research and Strategy department at CMR is tasked with researching, developing, evaluating, and documenting technologies for future use in surgical robotics. This covers the full range of technologies necessary to develop a complex mechatronic medical device. We’re looking for an amazing Data Engineer to join our Data Intelligence team, based in Cambridge, UK. This team helps engineers understand trends in Versius usage and provides robust data to guide business decisions.
- Help answer a broad range of data requests and communicate your findings.
- Build, test, and monitor data pipelines.
- Support the development and validation of reports and dashboards to analyse and track key performance indicators (KPI) and metrics for monitoring the health of the business, department, product, or processes.
- Bringing scientific and mathematical rigour to all elements of your work.
- Collaborating with a larger team of data scientists, data analysts, and data engineers.
- Remain up to date with the latest techniques in data engineering and data ETL.
- Contribute to improving the team’s processes and practices.
We’re a high growth company and roles can change and evolve. You’ll need to be willing to turn your hand to anything within the Data Intelligence team remit that supports the team with delivering its objectives.
To be successful in this role, you’ll need to have/be:
- A growth mindset, and the willingness to take ownership, make decisions, fail fast and learn from mistakes.
- Strong written and verbal communication skills.
- Experience with data querying, transformation, warehousing, and data lakes.
- Knowledge and practical experience of SQL and Spark.
- Proficiency in at least one modern object-orientated or functional programming language, preferably Python.
- Experience with managing cloud infrastructure. We use Terraform on AWS.
- Good understanding of data processing algorithms and bottlenecks.
It would also be helpful if you have:
- Experience with Databricks.
- Experience with data visualisation and dashboarding methods, tools, and libraries.
- Familiarity with data quality and security concepts.
- Experience working with data from medical devices.
- Good understanding of statistics fundamentals.