Data Engineering at Tessian As a high-growth scale-up, our email datasets are growing at an exponential rate. This is a great as it allows us to train best-in-class machine learning models to prevent previously unpreventable data breaches, however we're at the scale where our current data pipelines aren't where we want them to be: this is why we’re looking to bring another Data Engineer into the Tessian family. You will sit in our Data Engineering team and work day-to-day with our Data Scientists to build out infrastructure and pipelines, empowering teams to iterate quickly on terabytes of data. You'll be hugely impactful in this high-leverage role and we strongly believe that if we can query all of our data, in near real-time, and using scalable systems, then we can deliver limitless value to our clients through the data breaches we prevent.
Some interesting projects we’re working on:
- Building an Ingestion system to process Insights from different models using Kafka and Spark
- Designing the next generation data-lake setting ourselves up to handle massive future scale
- Creating a framework allowing us to standardise how we deploy all our ML models to production
We'd love to meet someone who:
- Is a highly-skilled developer who understands software engineering best practices (git, CI/CD, testing, reviewing, etc) and infrastructure as code principles.
- Has experience working with distributed data systems such as Spark
- Has designed and deployed data pipelines and ETL systems for data-at-scale
- Has a deep knowledge of the AWS ecosystem and have managed AWS production environments
- Has experience with scheduling systems like Airflow
- Ideally has been involved in machine learning infrastructure projects from automated training through to deployment
- Has an ability to break down ambiguous problems into concrete, manageable components and think through optimal solutions
- Enjoys “getting their hands dirty”by digging into complex operations
- Takes a high degree of ownership over their workIs a clear communicator with professional presence
- Has strong listening skills;open to input from other team members and departments
On a day-to-day basis you'll get to
- Build systems to efficiently handle our ever increasing volume of data
- Design and implement data pipelines as well as owning the vision of what our systems could achieve in the future
- Work with Data Scientists to train, version, test, deploy and monitor our machine learning models in production
- Design systems to expose data to our product and engineering teams in a performant way
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