Data Engineer at Tessian

we’re looking to bring another Data Engineer into the Tessian family.

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

Description

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

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