Collaborate across a variety of teams to enable our Data Platform needs and design, implement, and maintain a secure and scalable infrastructure platform spanning across AWS/Azure and our Data Center
Own and ensure that internal and external SLA’s meet and exceed expectations, System centric KPIs are continuously monitored
Work on our data lake, data warehouse, and stream processing systems to create a unified query engine, multi-model databases, analytics extracts and reports, as well as dashboard and visualizations
Design and build systems for high availability, high throughput, data consistency, security, and end user privacy, defining our next generation of data analytics tooling
You will mentor other engineers and promote software engineering best practices across the organization designing systems with monitoring, auditing, reliability, and security at their core
Come up with solutions for scaling data systems for various business needs and collaborate in a dynamic and consultative environment
Knowledge of at least one of the public cloud platforms AWS, Azure, or GCP is required.
Knowledge of a programming language - Python, Scala, or SQL
Knowledge of end-to-end data analytics workflow
Hands-on professional experience in Databricks
Excellent time management and prioritization skills
Excellent written and verbal communication
Bonus - Knowledge of Data Science and Machine Learning (e.g., build and deploy ML Models)
Core/Must Have Skills:
Knowledge of at least one of the public cloud platforms AWS or Azure is required.
Minimum 5+ years of Databricks experience using Azure Cloud
5+ years of hands on expertise in Hadoop and Scala
Practical experience with Data Engineering and the accompanying DevOps & DataOps workflows
Knowledge of a programming language - Python, Scala, or SQL
Knowledge of end-to-end data analytics workflow
Hands-on professional experience in Databricks
Excellent time management and prioritization skills
Excellent written and verbal communication
Bonus - Knowledge of Data Science and Machine Learning (e.g., build and deploy ML Models)
Strong end-to-end ownership and a good sense of urgency to enable proper self-prioritization
