Sr Data Engineer

BeyondTrust

54d

About The Position

BeyondTrust is a place where you can bring your purpose to life through the work that you do, creating a safer world through our cybersecurity SaaS portfolio. Our culture of flexibility, trust, and continual learning means you will be recognized for your growth, and for the impact you make on our success. You will be surrounded by people who challenge, support, and inspire you to be the best version of yourself. The Role As a Senior Data Engineer at BeyondTrust, you will help build and enhance our state of the art datalakehouse which is responsible for consuming billions of events each day. With security and computational efficiency at top of mind, you will help cut through the noise and create valuable, actionable insights from our vast quantity of data to deliver immediate value to our customers. Our engineers are problem solvers at heart and will tackle both business problems and technical engineering challenges alike with a focus on how & why before solutioning

Requirements

Strong programming and technology knowledge in cloud data processing.
Previous experience working in matured data lakes
Strong data modelling skills for analytical workloads.
Spark (or equivalent parallel processing framework) experience is needed, existing Databricks knowledge is a plus.
Interest and aptitude for cybersecurity; interest in identity security is highly preferred.
Technical understanding of underlying systems and computation minutiae.
Experience working with distributed systems and data processing on object stores.
Ability to work autonomously is a MUST.
Data warehousing for analytics use cases
Highly proficient in Python (or another object orientated language)
Distributed processing framework experience

Nice To Haves

Spark
Realtime processing experience.
Some experience with graph data stores

Responsibilities

Optimize data workloads at a software level by improving processing efficiency.
Develop new data processing routes to remove redundancy or reduce transformation overhead.
Perform complex data transformation in order to accomplish niche business use cases.
Develop new frameworks for data development to supercharge our delivery velocity.
Monitor and maintain existing data workflows.
Use observability best practices to ensure pipeline performance.
Perform complex transformations on both real time and batch data assets.
Create new ML/Engineering solutions to tackle existing issues in the cybersecurity space.
Leverage CI/CD best practices to effectively develop and release source code.

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