Strong knowledge of various database technologies ( SQL, NoSQL ), data integration (ETL/ELT application integration) frameworks, and cloud services (AWS, Azure, GCP). Experience with big data technologies ( Hadoop, Spark ) and data streaming services (Kafka.

1) Developing huge scale Data ingestion, Aggregation and Machine learning platform using ETL Design, Big Data, Spark, DataBricks, AWS, SnowFlake, and Data Lake; (3) Developing and maintaining Cloud Data Warehousing, Data lake using AWS S3, Snowflake, Blob Storage, and data security patterns.

Compute Fabric : Build the resource management infrastructure powering all the big data and machine learning workloads on the Databricks platform in a robust, flexible, secure, and cloud-agnostic way.

Strong knowledge of hybrid cloud, AWS, GCP, Azure and EntraID/Azure AD, OpenShift, Openstack Keystone. Deep understanding of cloud computing principles, including virtualization, containerization, microservices, and serverless computing; Risk Management, RHCOS security, container security, Kubernetes security, IAM security, network security, auditing, encryption, secrets management and data protection, securing CI/CD.

3+ years experience with big data and Security Information & Event Management (SIEM) solutions such as ArcSight, Splunk, ElasticSearch, Logstash, Azure Data Explorer, Azure Log Analytics, Azure Data Lake, or Azure Sentinel.

Role- Security Engineer IAM, Vault, Cloud & Kubernetes. Create Identity & Access as code leveraging tools such as ansible, terraform to provision in cloud. 3+ years of experience of cloud IAM and security experience.

Our big data solution leverages Spark, HBase, Sqoop, Oozie, and process messaging (Kafka). Experience with AWS cloud services: EC2, EMR, RDS, Redshift. We utilize event driven architectures leveraging a Kafka based Data Bus. Our code is released to production many times a week using our Continuous Deployment architecture.

Strong knowledge of hybrid cloud, AWS, GCP, Azure and Kurbenetes. Security Engineer - Cloud Security & Kurbenetes. Experience with Red Hat OpenShift and Openstack cloud platforms, Advanced cluster security, Advanced cluster management.

Quant UXRs vary in background using skills from computer science, quantitative social science, econometrics, data science, survey research, psychology, human-computer interaction, and other fields.

Kafka, Elastic Search, Cloud Native, Kubernetes, GCP. RUST - Data Processing. Data Engineering + Backend Engineering. DevOps: Creating automated CI-CD processes using containerization solutions (Docker), CI tools (Drone, Jenkins, Spinnaker), Orchestration (Kubernetes, Helm) and Infrastructure automation (ArgoCD, Rancher, Ansible, Terraform, Chef.

Design, implement, and support data warehouse / data lake infrastructure using AWS big data stack, Python, Redshift, Quicksight, Glue/lake formation, EMR/Spark/Scala, Athena etc.

Utilize AWS cloud services such as EMR, Redshift, and Spark to build and optimize big data solutions. Strong experience with AWS cloud services, including EMR, Redshift, and other data processing technologies.

Professional Experience: At least 5 years of experience in data engineering, with a strong focus on data integration and pipeline construction in cloud environments and enterprise data warehouses like Snowflake.

On the PADI team, you will be responsible for identifying opportunities to improve player and creator experiences from telemetry using big data tools, and partner independently and seamlessly, aligning and influencing to create action on insights.

Experience with cloud infrastructure as code tools such as Terraform, CloudFormation, and Azure Resource Manager. Security Engineers with primary focus on Cloud Security with Kubernetes combination.